2022-04-19 10:51:23 INFO i.a.w.w.WorkerRun(call):49 - Executing worker wrapper. Airbyte version: 0.35.30-alpha
2022-04-19 10:51:23 INFO i.a.w.t.TemporalAttemptExecution(get):105 - Docker volume job log path: /tmp/workspace/37/0/logs.log
2022-04-19 10:51:23 INFO i.a.w.t.TemporalAttemptExecution(get):110 - Executing worker wrapper. Airbyte version: 0.35.30-alpha
2022-04-19 10:51:23 INFO i.a.w.DefaultReplicationWorker(run):103 - start sync worker. job id: 37 attempt id: 0
2022-04-19 10:51:23 INFO i.a.w.DefaultReplicationWorker(run):115 - configured sync modes: {null.tickets=incremental - append_dedup, null.sla_policies=full_refresh - append, null.brands=full_refresh - append, null.ticket_fields=incremental - append_dedup, null.ticket_metric_events=incremental - append_dedup, null.ticket_metrics=incremental - append_dedup, null.tags=full_refresh - append}
2022-04-19 10:51:23 INFO i.a.w.p.a.DefaultAirbyteDestination(start):69 - Running destination...
2022-04-19 10:51:23 INFO i.a.c.i.LineGobbler(voidCall):82 - Checking if airbyte/destination-redshift:0.3.28 exists...
2022-04-19 10:51:24 INFO i.a.c.i.LineGobbler(voidCall):82 - airbyte/destination-redshift:0.3.28 was found locally.
2022-04-19 10:51:24 INFO i.a.w.p.DockerProcessFactory(create):157 - Preparing command: docker run --rm --init -i -w /data/37/0 --log-driver none --network host -v airbyte_workspace:/data -v /tmp/airbyte_local:/local airbyte/destination-redshift:0.3.28 write --config destination_config.json --catalog destination_catalog.json
2022-04-19 10:51:24 INFO i.a.c.i.LineGobbler(voidCall):82 - Checking if airbyte/source-zendesk-support:0.2.5 exists...
2022-04-19 10:51:24 INFO i.a.c.i.LineGobbler(voidCall):82 - airbyte/source-zendesk-support:0.2.5 was found locally.
2022-04-19 10:51:24 INFO i.a.w.p.DockerProcessFactory(create):157 - Preparing command: docker run --rm --init -i -w /data/37/0 --log-driver none --network host -v airbyte_workspace:/data -v /tmp/airbyte_local:/local airbyte/source-zendesk-support:0.2.5 read --config source_config.json --catalog source_catalog.json --state input_state.json
2022-04-19 10:51:24 INFO i.a.w.DefaultReplicationWorker(run):157 - Waiting for source and destination threads to complete.
2022-04-19 10:51:24 INFO i.a.w.DefaultReplicationWorker(lambda$getDestinationOutputRunnable$6):337 - Destination output thread started.
2022-04-19 10:51:24 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):278 - Replication thread started.
2022-04-19 10:51:25 destination > SLF4J: Class path contains multiple SLF4J bindings.
2022-04-19 10:51:25 destination > SLF4J: Found binding in [jar:file:/airbyte/lib/log4j-slf4j-impl-2.17.1.jar!/org/slf4j/impl/StaticLoggerBinder.class]
2022-04-19 10:51:25 destination > SLF4J: Found binding in [jar:file:/airbyte/lib/slf4j-log4j12-1.7.25.jar!/org/slf4j/impl/StaticLoggerBinder.class]
2022-04-19 10:51:25 destination > SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
2022-04-19 10:51:25 destination > SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]
2022-04-19 10:51:26 source > Starting syncing SourceZendeskSupport
2022-04-19 10:51:26 source > Syncing stream: brands 
2022-04-19 10:51:26 source > Read 12 records from brands stream
2022-04-19 10:51:26 source > Finished syncing brands
2022-04-19 10:51:26 source > SourceZendeskSupport runtimes:
Syncing stream brands 0:00:00.375589
2022-04-19 10:51:26 source > Syncing stream: sla_policies 
2022-04-19 10:51:26 source > Read 11 records from sla_policies stream
2022-04-19 10:51:26 source > Finished syncing sla_policies
2022-04-19 10:51:26 source > SourceZendeskSupport runtimes:
Syncing stream brands 0:00:00.375589
Syncing stream sla_policies 0:00:00.205593
2022-04-19 10:51:26 source > Syncing stream: tags 
2022-04-19 10:51:27 source > Read 196 records from tags stream
2022-04-19 10:51:27 source > Finished syncing tags
2022-04-19 10:51:27 source > SourceZendeskSupport runtimes:
Syncing stream brands 0:00:00.375589
Syncing stream sla_policies 0:00:00.205593
Syncing stream tags 0:00:00.420369
2022-04-19 10:51:27 source > Syncing stream: ticket_fields 
2022-04-19 10:51:27 destination > 2022-04-19 10:51:27 INFO i.a.i.d.r.RedshiftDestination(main):77 - starting destination: class io.airbyte.integrations.destination.redshift.RedshiftDestination
2022-04-19 10:51:27 source > Read 14 records from ticket_fields stream
2022-04-19 10:51:27 source > Finished syncing ticket_fields
2022-04-19 10:51:27 source > SourceZendeskSupport runtimes:
Syncing stream brands 0:00:00.375589
Syncing stream sla_policies 0:00:00.205593
Syncing stream tags 0:00:00.420369
Syncing stream ticket_fields 0:00:00.389495
2022-04-19 10:51:27 source > Syncing stream: ticket_metric_events 
2022-04-19 10:51:27 destination > 2022-04-19 10:51:27 INFO i.a.i.b.IntegrationCliParser(parseOptions):118 - integration args: {catalog=destination_catalog.json, write=null, config=destination_config.json}
2022-04-19 10:51:27 destination > 2022-04-19 10:51:27 INFO i.a.i.b.IntegrationRunner(runInternal):121 - Running integration: io.airbyte.integrations.destination.redshift.RedshiftDestination
2022-04-19 10:51:27 destination > 2022-04-19 10:51:27 INFO i.a.i.b.IntegrationRunner(runInternal):122 - Command: WRITE
2022-04-19 10:51:27 destination > 2022-04-19 10:51:27 INFO i.a.i.b.IntegrationRunner(runInternal):123 - Integration config: IntegrationConfig{command=WRITE, configPath='destination_config.json', catalogPath='destination_catalog.json', statePath='null'}
2022-04-19 10:51:28 destination > 2022-04-19 10:51:28 WARN c.n.s.JsonMetaSchema(newValidator):338 - Unknown keyword examples - you should define your own Meta Schema. If the keyword is irrelevant for validation, just use a NonValidationKeyword
2022-04-19 10:51:28 destination > 2022-04-19 10:51:28 WARN c.n.s.JsonMetaSchema(newValidator):338 - Unknown keyword airbyte_secret - you should define your own Meta Schema. If the keyword is irrelevant for validation, just use a NonValidationKeyword
2022-04-19 10:51:28 destination > 2022-04-19 10:51:28 INFO i.a.i.d.j.c.SwitchingDestination(getConsumer):65 - Using destination type: COPY_S3
2022-04-19 10:51:28 destination > 2022-04-19 10:51:28 INFO i.a.i.d.s.S3DestinationConfig(createS3Client):169 - Creating S3 client...
2022-04-19 10:51:29 destination > 2022-04-19 10:51:29 INFO i.a.i.d.b.BufferedStreamConsumer(startTracked):141 - class io.airbyte.integrations.destination.buffered_stream_consumer.BufferedStreamConsumer started.
2022-04-19 10:51:29 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 1000
2022-04-19 10:51:29 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 2000
2022-04-19 10:51:30 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 3000
2022-04-19 10:51:30 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 4000
2022-04-19 10:51:30 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 5000
2022-04-19 10:51:31 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 6000
2022-04-19 10:51:31 source > Read 6084 records from ticket_metric_events stream
2022-04-19 10:51:31 source > Finished syncing ticket_metric_events
2022-04-19 10:51:31 source > SourceZendeskSupport runtimes:
Syncing stream brands 0:00:00.375589
Syncing stream sla_policies 0:00:00.205593
Syncing stream tags 0:00:00.420369
Syncing stream ticket_fields 0:00:00.389495
Syncing stream ticket_metric_events 0:00:03.860808
2022-04-19 10:51:31 source > Syncing stream: ticket_metrics 
2022-04-19 10:51:33 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 7000
2022-04-19 10:51:34 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 8000
2022-04-19 10:51:35 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 9000
2022-04-19 10:51:36 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 10000
2022-04-19 10:51:38 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 11000
2022-04-19 10:51:39 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 12000
2022-04-19 10:51:40 destination > 2022-04-19 10:51:40 INFO i.a.i.d.b.BufferedStreamConsumer(flushQueueToDestination):181 - Flushing buffer: 26211448 bytes
2022-04-19 10:51:40 destination > 2022-04-19 10:51:40 INFO i.a.i.d.b.BufferedStreamConsumer(lambda$flushQueueToDestination$1):185 - Flushing brands: 12 records
2022-04-19 10:51:40 destination > 2022-04-19 10:51:40 INFO i.a.i.d.j.c.s.S3StreamCopier(prepareStagingFile):95 - S3 upload part size: 10 MB
2022-04-19 10:51:40 destination > 2022-04-19 10:51:40 INFO i.a.i.d.s.c.S3CsvWriter(<init>):58 - Full S3 path for stream 'brands': s3://travlrdatatesting/airbyte_data/brands/2022_04_19_1650365489228_9dc06c5f-d8a7-4901-b41d-a9c8e16df070.csv
2022-04-19 10:51:40 destination > 2022-04-19 10:51:40 INFO i.a.i.d.s.u.S3StreamTransferManagerHelper(getDefault):55 - PartSize arg is set to 10 MB
2022-04-19 10:51:41 destination > 2022-04-19 10:51:41 INFO a.m.s.StreamTransferManager(getMultiPartOutputStreams):329 - Initiated multipart upload to travlrdatatesting/airbyte_data/brands/2022_04_19_1650365489228_9dc06c5f-d8a7-4901-b41d-a9c8e16df070.csv with full ID zgP5ZcAshP46IQKgRJOEzz3beo5dDDUzfbR4CJXmzQQ35TlHNG_uwYhctYK3Ajj5.q9TGjfAEoHZ4bp8zvZQkET6G_c92t5hDdvQcTDWcpJflGgIZ_ovQwpeSVi3bc.8OSms14eDv_ehZkeojN7UxA--
2022-04-19 10:51:41 destination > 2022-04-19 10:51:41 INFO i.a.i.d.b.BufferedStreamConsumer(lambda$flushQueueToDestination$1):185 - Flushing sla_policies: 11 records
2022-04-19 10:51:41 destination > 2022-04-19 10:51:41 INFO i.a.i.d.j.c.s.S3StreamCopier(prepareStagingFile):95 - S3 upload part size: 10 MB
2022-04-19 10:51:41 destination > 2022-04-19 10:51:41 INFO i.a.i.d.s.c.S3CsvWriter(<init>):58 - Full S3 path for stream 'sla_policies': s3://travlrdatatesting/airbyte_data/sla_policies/2022_04_19_1650365489230_b4ec8c47-505c-4324-b71d-383b700bcbc5.csv
2022-04-19 10:51:41 destination > 2022-04-19 10:51:41 INFO i.a.i.d.s.u.S3StreamTransferManagerHelper(getDefault):55 - PartSize arg is set to 10 MB
2022-04-19 10:51:41 destination > 2022-04-19 10:51:41 INFO a.m.s.StreamTransferManager(getMultiPartOutputStreams):329 - Initiated multipart upload to travlrdatatesting/airbyte_data/sla_policies/2022_04_19_1650365489230_b4ec8c47-505c-4324-b71d-383b700bcbc5.csv with full ID G0Ap2L3wiOdjIn_DFwC8bfTqx8TNhtwQs5hlHl8W1.Gpztzy5DDcF2EQYREz.2Uu5O6_KXL.i_RDfbsx3TQ0oCeD_X3J6lT7_pO2Fv6Q_wl6XHviWBwT6CqIJ.8._QqI82fkrQB.FG6AOrDrP7HD2A--
2022-04-19 10:51:41 destination > 2022-04-19 10:51:41 INFO i.a.i.d.b.BufferedStreamConsumer(lambda$flushQueueToDestination$1):185 - Flushing ticket_metrics: 6129 records
2022-04-19 10:51:41 destination > 2022-04-19 10:51:41 INFO i.a.i.d.j.c.s.S3StreamCopier(prepareStagingFile):95 - S3 upload part size: 10 MB
2022-04-19 10:51:41 destination > 2022-04-19 10:51:41 INFO i.a.i.d.s.c.S3CsvWriter(<init>):58 - Full S3 path for stream 'ticket_metrics': s3://travlrdatatesting/airbyte_data/ticket_metrics/2022_04_19_1650365489231_3834c0e3-6f70-4716-9b22-01ea5c83901e.csv
2022-04-19 10:51:41 destination > 2022-04-19 10:51:41 INFO i.a.i.d.s.u.S3StreamTransferManagerHelper(getDefault):55 - PartSize arg is set to 10 MB
2022-04-19 10:51:41 destination > 2022-04-19 10:51:41 INFO a.m.s.StreamTransferManager(getMultiPartOutputStreams):329 - Initiated multipart upload to travlrdatatesting/airbyte_data/ticket_metrics/2022_04_19_1650365489231_3834c0e3-6f70-4716-9b22-01ea5c83901e.csv with full ID FDfzzvyL82moUTZU.Squ7htUskVKi8BecBYkN59Xqm_jz9.29gCJyazZRA9nUH5PHES9X_2PAE13B.iB5VxUv.hnI_zpa5T7UZ0oTDDcjpkXlBoyOEkGF12fgiAbHZuAUaYwl91KFfEvHcqFBbgxnw--
2022-04-19 10:51:42 destination > 2022-04-19 10:51:42 INFO i.a.i.d.b.BufferedStreamConsumer(lambda$flushQueueToDestination$1):185 - Flushing ticket_metric_events: 6084 records
2022-04-19 10:51:42 destination > 2022-04-19 10:51:42 INFO i.a.i.d.j.c.s.S3StreamCopier(prepareStagingFile):95 - S3 upload part size: 10 MB
2022-04-19 10:51:42 destination > 2022-04-19 10:51:42 INFO i.a.i.d.s.c.S3CsvWriter(<init>):58 - Full S3 path for stream 'ticket_metric_events': s3://travlrdatatesting/airbyte_data/ticket_metric_events/2022_04_19_1650365489231_dec743cb-f6b0-4fac-8244-152444fd5a6d.csv
2022-04-19 10:51:42 destination > 2022-04-19 10:51:42 INFO i.a.i.d.s.u.S3StreamTransferManagerHelper(getDefault):55 - PartSize arg is set to 10 MB
2022-04-19 10:51:43 destination > 2022-04-19 10:51:43 INFO a.m.s.StreamTransferManager(getMultiPartOutputStreams):329 - Initiated multipart upload to travlrdatatesting/airbyte_data/ticket_metric_events/2022_04_19_1650365489231_dec743cb-f6b0-4fac-8244-152444fd5a6d.csv with full ID 2RaWFw_1RfSi44HR_5_AUpHGJOvTAFHk9wGNkziBnrgT.EBzE1_hjIkCkPnTra4eSARfJs3Vgcl.tUpqsZJ7IiOvWaFG_eLxNTu1YtgUaf.DlW_LKOG67Nmz.h3SffkMaCz5FxpN0m.7sAyWC2H2Jg--
2022-04-19 10:51:43 destination > 2022-04-19 10:51:43 INFO i.a.i.d.b.BufferedStreamConsumer(lambda$flushQueueToDestination$1):185 - Flushing tags: 196 records
2022-04-19 10:51:43 destination > 2022-04-19 10:51:43 INFO i.a.i.d.j.c.s.S3StreamCopier(prepareStagingFile):95 - S3 upload part size: 10 MB
2022-04-19 10:51:43 destination > 2022-04-19 10:51:43 INFO i.a.i.d.s.c.S3CsvWriter(<init>):58 - Full S3 path for stream 'tags': s3://travlrdatatesting/airbyte_data/tags/2022_04_19_1650365489230_ddd5b656-ef4f-4aa1-90e1-9b4e351289a9.csv
2022-04-19 10:51:43 destination > 2022-04-19 10:51:43 INFO i.a.i.d.s.u.S3StreamTransferManagerHelper(getDefault):55 - PartSize arg is set to 10 MB
2022-04-19 10:51:43 destination > 2022-04-19 10:51:43 INFO a.m.s.StreamTransferManager(getMultiPartOutputStreams):329 - Initiated multipart upload to travlrdatatesting/airbyte_data/tags/2022_04_19_1650365489230_ddd5b656-ef4f-4aa1-90e1-9b4e351289a9.csv with full ID sI6BWXhW1gV8WHJ4n0F7zVvOI6A8QQ.ztyTfuijDOIujgo_baEa4j_FU.Vu941RgBXNVmO3viXMRYmEPrNk6zkZtkgqtRjCMnuOjBOuT5GKMe8NIqfsYPyqCxt3XlN5i.Zfi7ys8_QcF6DBhYibb5g--
2022-04-19 10:51:43 destination > 2022-04-19 10:51:43 INFO i.a.i.d.b.BufferedStreamConsumer(lambda$flushQueueToDestination$1):185 - Flushing ticket_fields: 14 records
2022-04-19 10:51:43 destination > 2022-04-19 10:51:43 INFO i.a.i.d.j.c.s.S3StreamCopier(prepareStagingFile):95 - S3 upload part size: 10 MB
2022-04-19 10:51:43 destination > 2022-04-19 10:51:43 INFO i.a.i.d.s.c.S3CsvWriter(<init>):58 - Full S3 path for stream 'ticket_fields': s3://travlrdatatesting/airbyte_data/ticket_fields/2022_04_19_1650365489230_b6d71c3d-1fd6-4c31-8899-d726045b84f2.csv
2022-04-19 10:51:43 destination > 2022-04-19 10:51:43 INFO i.a.i.d.s.u.S3StreamTransferManagerHelper(getDefault):55 - PartSize arg is set to 10 MB
2022-04-19 10:51:43 destination > 2022-04-19 10:51:43 INFO a.m.s.StreamTransferManager(getMultiPartOutputStreams):329 - Initiated multipart upload to travlrdatatesting/airbyte_data/ticket_fields/2022_04_19_1650365489230_b6d71c3d-1fd6-4c31-8899-d726045b84f2.csv with full ID RnquPRcCjT07TZfnyFAEmgCQi0PUtnnWsycMxFZsSHBXcfK6iepxROxm1D1FZuc7frm7H0VWUSBjYHj081RpH.cSffwKlT6SYPrexuhWEe0R8AemNtAdIxuYyLCAvm0HjhZUrhl.YQkjvFfh7PMoHQ--
2022-04-19 10:51:43 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 13000
2022-04-19 10:51:43 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 14000
2022-04-19 10:51:44 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 15000
2022-04-19 10:51:52 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 16000
2022-04-19 10:52:09 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 17000
2022-04-19 10:52:17 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 18000
2022-04-19 10:52:27 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 19000
2022-04-19 10:52:33 destination > 2022-04-19 10:52:33 INFO i.a.i.d.b.BufferedStreamConsumer(flushQueueToDestination):181 - Flushing buffer: 26213738 bytes
2022-04-19 10:52:33 destination > 2022-04-19 10:52:33 INFO i.a.i.d.b.BufferedStreamConsumer(lambda$flushQueueToDestination$1):185 - Flushing ticket_metrics: 7196 records
2022-04-19 10:52:35 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 20000
2022-04-19 10:52:44 INFO i.a.w.DefaultReplicationWorker(lambda$getReplicationRunnable$5):300 - Records read: 21000
2022-04-19 13:13:15 source > Encountered an exception while reading stream ticket_metrics
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 710, in urlopen
    chunked=chunked,
  File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 449, in _make_request
    six.raise_from(e, None)
  File "<string>", line 3, in raise_from
  File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 444, in _make_request
    httplib_response = conn.getresponse()
  File "/usr/local/lib/python3.7/http/client.py", line 1373, in getresponse
    response.begin()
  File "/usr/local/lib/python3.7/http/client.py", line 319, in begin
    version, status, reason = self._read_status()
  File "/usr/local/lib/python3.7/http/client.py", line 280, in _read_status
    line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
  File "/usr/local/lib/python3.7/socket.py", line 589, in readinto
    return self._sock.recv_into(b)
  File "/usr/local/lib/python3.7/ssl.py", line 1071, in recv_into
    return self.read(nbytes, buffer)
  File "/usr/local/lib/python3.7/ssl.py", line 929, in read
    return self._sslobj.read(len, buffer)
ConnectionResetError: [Errno 104] Connection reset by peer

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/requests/adapters.py", line 450, in send
    timeout=timeout
  File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 786, in urlopen
    method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
  File "/usr/local/lib/python3.7/site-packages/urllib3/util/retry.py", line 550, in increment
    raise six.reraise(type(error), error, _stacktrace)
  File "/usr/local/lib/python3.7/site-packages/urllib3/packages/six.py", line 769, in reraise
    raise value.with_traceback(tb)
  File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 710, in urlopen
    chunked=chunked,
  File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 449, in _make_request
    six.raise_from(e, None)
  File "<string>", line 3, in raise_from
  File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 444, in _make_request
    httplib_response = conn.getresponse()
  File "/usr/local/lib/python3.7/http/client.py", line 1373, in getresponse
    response.begin()
  File "/usr/local/lib/python3.7/http/client.py", line 319, in begin
    version, status, reason = self._read_status()
  File "/usr/local/lib/python3.7/http/client.py", line 280, in _read_status
    line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
  File "/usr/local/lib/python3.7/socket.py", line 589, in readinto
    return self._sock.recv_into(b)
  File "/usr/local/lib/python3.7/ssl.py", line 1071, in recv_into
    return self.read(nbytes, buffer)
  File "/usr/local/lib/python3.7/ssl.py", line 929, in read
    return self._sslobj.read(len, buffer)
urllib3.exceptions.ProtocolError: ('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/airbyte_cdk/sources/abstract_source.py", line 119, in read
    internal_config=internal_config,
  File "/usr/local/lib/python3.7/site-packages/airbyte_cdk/sources/abstract_source.py", line 159, in _read_stream
    for record in record_iterator:
  File "/usr/local/lib/python3.7/site-packages/airbyte_cdk/sources/abstract_source.py", line 215, in _read_incremental
    for record_counter, record_data in enumerate(records, start=1):
  File "/airbyte/integration_code/source_zendesk_support/streams.py", line 267, in read_records
    response = item["future"].result()
  File "/usr/local/lib/python3.7/concurrent/futures/_base.py", line 428, in result
    return self.__get_result()
  File "/usr/local/lib/python3.7/concurrent/futures/_base.py", line 384, in __get_result
    raise self._exception
  File "/usr/local/lib/python3.7/concurrent/futures/thread.py", line 57, in run
    result = self.fn(*self.args, **self.kwargs)
  File "/usr/local/lib/python3.7/site-packages/requests/sessions.py", line 645, in send
    r = adapter.send(request, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/requests/adapters.py", line 501, in send
    raise ConnectionError(err, request=request)
requests.exceptions.ConnectionError: ('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))
2022-04-19 13:13:15 source > Finished syncing ticket_metrics
2022-04-19 13:13:15 source > SourceZendeskSupport runtimes:
Syncing stream brands 0:00:00.375589
Syncing stream sla_policies 0:00:00.205593
Syncing stream tags 0:00:00.420369
Syncing stream ticket_fields 0:00:00.389495
Syncing stream ticket_metric_events 0:00:03.860808
Syncing stream ticket_metrics 2:21:44.470708
2022-04-19 13:13:15 source > ('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 710, in urlopen
    chunked=chunked,
  File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 449, in _make_request
    six.raise_from(e, None)
  File "<string>", line 3, in raise_from
  File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 444, in _make_request
    httplib_response = conn.getresponse()
  File "/usr/local/lib/python3.7/http/client.py", line 1373, in getresponse
    response.begin()
  File "/usr/local/lib/python3.7/http/client.py", line 319, in begin
    version, status, reason = self._read_status()
  File "/usr/local/lib/python3.7/http/client.py", line 280, in _read_status
    line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
  File "/usr/local/lib/python3.7/socket.py", line 589, in readinto
    return self._sock.recv_into(b)
  File "/usr/local/lib/python3.7/ssl.py", line 1071, in recv_into
    return self.read(nbytes, buffer)
  File "/usr/local/lib/python3.7/ssl.py", line 929, in read
    return self._sslobj.read(len, buffer)
ConnectionResetError: [Errno 104] Connection reset by peer

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/requests/adapters.py", line 450, in send
    timeout=timeout
  File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 786, in urlopen
    method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
  File "/usr/local/lib/python3.7/site-packages/urllib3/util/retry.py", line 550, in increment
    raise six.reraise(type(error), error, _stacktrace)
  File "/usr/local/lib/python3.7/site-packages/urllib3/packages/six.py", line 769, in reraise
    raise value.with_traceback(tb)
  File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 710, in urlopen
    chunked=chunked,
  File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 449, in _make_request
    six.raise_from(e, None)
  File "<string>", line 3, in raise_from
  File "/usr/local/lib/python3.7/site-packages/urllib3/connectionpool.py", line 444, in _make_request
    httplib_response = conn.getresponse()
  File "/usr/local/lib/python3.7/http/client.py", line 1373, in getresponse
    response.begin()
  File "/usr/local/lib/python3.7/http/client.py", line 319, in begin
    version, status, reason = self._read_status()
  File "/usr/local/lib/python3.7/http/client.py", line 280, in _read_status
    line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
  File "/usr/local/lib/python3.7/socket.py", line 589, in readinto
    return self._sock.recv_into(b)
  File "/usr/local/lib/python3.7/ssl.py", line 1071, in recv_into
    return self.read(nbytes, buffer)
  File "/usr/local/lib/python3.7/ssl.py", line 929, in read
    return self._sslobj.read(len, buffer)
urllib3.exceptions.ProtocolError: ('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/airbyte/integration_code/main.py", line 13, in <module>
    launch(source, sys.argv[1:])
  File "/usr/local/lib/python3.7/site-packages/airbyte_cdk/entrypoint.py", line 127, in launch
    for message in source_entrypoint.run(parsed_args):
  File "/usr/local/lib/python3.7/site-packages/airbyte_cdk/entrypoint.py", line 118, in run
    for message in generator:
  File "/usr/local/lib/python3.7/site-packages/airbyte_cdk/sources/abstract_source.py", line 123, in read
    raise e
  File "/usr/local/lib/python3.7/site-packages/airbyte_cdk/sources/abstract_source.py", line 119, in read
    internal_config=internal_config,
  File "/usr/local/lib/python3.7/site-packages/airbyte_cdk/sources/abstract_source.py", line 159, in _read_stream
    for record in record_iterator:
  File "/usr/local/lib/python3.7/site-packages/airbyte_cdk/sources/abstract_source.py", line 215, in _read_incremental
    for record_counter, record_data in enumerate(records, start=1):
  File "/airbyte/integration_code/source_zendesk_support/streams.py", line 267, in read_records
    response = item["future"].result()
  File "/usr/local/lib/python3.7/concurrent/futures/_base.py", line 428, in result
    return self.__get_result()
  File "/usr/local/lib/python3.7/concurrent/futures/_base.py", line 384, in __get_result
    raise self._exception
  File "/usr/local/lib/python3.7/concurrent/futures/thread.py", line 57, in run
    result = self.fn(*self.args, **self.kwargs)
  File "/usr/local/lib/python3.7/site-packages/requests/sessions.py", line 645, in send
    r = adapter.send(request, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/requests/adapters.py", line 501, in send
    raise ConnectionError(err, request=request)
requests.exceptions.ConnectionError: ('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))
2022-04-19 13:13:17 destination > 2022-04-19 13:13:17 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):229 - The main thread is exiting while children non-daemon threads from a connector are still active.
2022-04-19 13:13:17 destination > Ideally, this situation should not happen...
2022-04-19 13:13:17 destination > Please check with maintainers if the connector or library code should safely clean up its threads before quitting instead.
2022-04-19 13:13:17 destination > The main thread is: main (RUNNABLE)
2022-04-19 13:13:17 destination >  Thread stacktrace: java.base/java.lang.Thread.getStackTrace(Thread.java:1610)
2022-04-19 13:13:17 destination >         at io.airbyte.integrations.base.IntegrationRunner.dumpThread(IntegrationRunner.java:264)
2022-04-19 13:13:17 destination >         at io.airbyte.integrations.base.IntegrationRunner.watchForOrphanThreads(IntegrationRunner.java:233)
2022-04-19 13:13:17 destination >         at io.airbyte.integrations.base.IntegrationRunner.runConsumer(IntegrationRunner.java:190)
2022-04-19 13:13:17 destination >         at io.airbyte.integrations.base.IntegrationRunner.lambda$runInternal$1(IntegrationRunner.java:163)
2022-04-19 13:13:17 destination >         at io.airbyte.integrations.base.sentry.AirbyteSentry.executeWithTracing(AirbyteSentry.java:54)
2022-04-19 13:13:17 destination >         at io.airbyte.integrations.base.sentry.AirbyteSentry.executeWithTracing(AirbyteSentry.java:38)
2022-04-19 13:13:17 destination >         at io.airbyte.integrations.base.IntegrationRunner.runInternal(IntegrationRunner.java:163)
2022-04-19 13:13:17 destination >         at io.airbyte.integrations.base.IntegrationRunner.run(IntegrationRunner.java:105)
2022-04-19 13:13:17 destination >         at io.airbyte.integrations.destination.redshift.RedshiftDestination.main(RedshiftDestination.java:78)
2022-04-19 13:13:17 destination > 2022-04-19 13:13:17 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-1 (WAITING)
2022-04-19 13:13:17 destination >  Thread stacktrace: java.base@17.0.1/jdk.internal.misc.Unsafe.park(Native Method)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.locks.LockSupport.park(LockSupport.java:341)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionNode.block(AbstractQueuedSynchronizer.java:506)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.unmanagedBlock(ForkJoinPool.java:3463)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.managedBlock(ForkJoinPool.java:3434)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1623)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.ArrayBlockingQueue.take(ArrayBlockingQueue.java:420)
2022-04-19 13:13:17 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:484)
2022-04-19 13:13:17 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:17 destination > 2022-04-19 13:13:17 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-2 (BLOCKED)
2022-04-19 13:13:17 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:17 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:17 destination > 2022-04-19 13:13:17 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-3 (BLOCKED)
2022-04-19 13:13:17 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:17 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:17 destination > 2022-04-19 13:13:17 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-4 (BLOCKED)
2022-04-19 13:13:17 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:17 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:17 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-5 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-6 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-7 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-8 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-9 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-2-thread-10 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-1 (WAITING)
2022-04-19 13:13:18 destination >  Thread stacktrace: java.base@17.0.1/jdk.internal.misc.Unsafe.park(Native Method)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.locks.LockSupport.park(LockSupport.java:341)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionNode.block(AbstractQueuedSynchronizer.java:506)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.unmanagedBlock(ForkJoinPool.java:3463)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.managedBlock(ForkJoinPool.java:3434)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1623)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ArrayBlockingQueue.take(ArrayBlockingQueue.java:420)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:484)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-2 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-3 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-4 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-5 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-6 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-7 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-8 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-9 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-3-thread-10 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-1 (WAITING)
2022-04-19 13:13:18 destination >  Thread stacktrace: java.base@17.0.1/jdk.internal.misc.Unsafe.park(Native Method)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.locks.LockSupport.park(LockSupport.java:341)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionNode.block(AbstractQueuedSynchronizer.java:506)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.unmanagedBlock(ForkJoinPool.java:3463)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.managedBlock(ForkJoinPool.java:3434)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1623)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ArrayBlockingQueue.take(ArrayBlockingQueue.java:420)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:484)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-2 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-3 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-4 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-5 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-6 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-7 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-8 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-9 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-4-thread-10 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-1 (WAITING)
2022-04-19 13:13:18 destination >  Thread stacktrace: java.base@17.0.1/jdk.internal.misc.Unsafe.park(Native Method)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.locks.LockSupport.park(LockSupport.java:341)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionNode.block(AbstractQueuedSynchronizer.java:506)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.unmanagedBlock(ForkJoinPool.java:3463)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.managedBlock(ForkJoinPool.java:3434)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1623)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ArrayBlockingQueue.take(ArrayBlockingQueue.java:420)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:484)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-2 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-3 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-4 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-5 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-6 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-7 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-8 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-9 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-5-thread-10 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-1 (WAITING)
2022-04-19 13:13:18 destination >  Thread stacktrace: java.base@17.0.1/jdk.internal.misc.Unsafe.park(Native Method)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.locks.LockSupport.park(LockSupport.java:341)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionNode.block(AbstractQueuedSynchronizer.java:506)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.unmanagedBlock(ForkJoinPool.java:3463)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.managedBlock(ForkJoinPool.java:3434)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1623)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ArrayBlockingQueue.take(ArrayBlockingQueue.java:420)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:484)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-2 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-3 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-4 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-5 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-6 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-7 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-8 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-9 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-6-thread-10 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-1 (WAITING)
2022-04-19 13:13:18 destination >  Thread stacktrace: java.base@17.0.1/jdk.internal.misc.Unsafe.park(Native Method)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.locks.LockSupport.park(LockSupport.java:341)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionNode.block(AbstractQueuedSynchronizer.java:506)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.unmanagedBlock(ForkJoinPool.java:3463)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ForkJoinPool.managedBlock(ForkJoinPool.java:3434)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1623)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ArrayBlockingQueue.take(ArrayBlockingQueue.java:420)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:484)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-2 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-3 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-4 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-5 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:18 destination > 2022-04-19 13:13:18 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-6 (BLOCKED)
2022-04-19 13:13:18 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:18 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:18 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:19 destination > 2022-04-19 13:13:19 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-7 (BLOCKED)
2022-04-19 13:13:19 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:19 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:19 destination > 2022-04-19 13:13:19 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-8 (BLOCKED)
2022-04-19 13:13:19 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:19 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:19 destination > 2022-04-19 13:13:19 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-9 (BLOCKED)
2022-04-19 13:13:19 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:19 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:19 destination > 2022-04-19 13:13:19 WARN i.a.i.b.IntegrationRunner(watchForOrphanThreads):243 - Active non-daemon thread: pool-7-thread-10 (BLOCKED)
2022-04-19 13:13:19 destination >  Thread stacktrace: app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:483)
2022-04-19 13:13:19 destination >         at app//alex.mojaki.s3upload.StreamTransferManager$UploadTask.call(StreamTransferManager.java:474)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:539)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.FutureTask.run(FutureTask.java:264)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
2022-04-19 13:13:19 destination >         at java.base@17.0.1/java.lang.Thread.run(Thread.java:833)
2022-04-19 13:13:19 destination > 2022-04-19 13:13:19 INFO i.a.i.b.FailureTrackingAirbyteMessageConsumer(close):65 - Airbyte message consumer: succeeded.
2022-04-19 13:13:19 destination > 2022-04-19 13:13:19 INFO i.a.i.d.b.BufferedStreamConsumer(close):217 - executing on success close procedure.
2022-04-19 13:13:19 destination > 2022-04-19 13:13:19 INFO i.a.i.d.b.BufferedStreamConsumer(flushQueueToDestination):181 - Flushing buffer: 5285678 bytes
2022-04-19 13:13:19 destination > 2022-04-19 13:13:19 INFO i.a.i.d.b.BufferedStreamConsumer(lambda$flushQueueToDestination$1):185 - Flushing ticket_metrics: 1451 records
2022-04-19 13:13:19 destination > 2022-04-19 13:13:19 INFO i.a.i.d.s.w.BaseS3Writer(close):113 - Uploading remaining data for stream 'brands'.
2022-04-19 13:13:19 destination > 2022-04-19 13:13:19 INFO a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-19 13:13:19 destination > 2022-04-19 13:13:19 INFO a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-19 13:13:19 destination > 2022-04-19 13:13:19 WARN a.m.s.MultiPartOutputStream(close):160 - [MultipartOutputStream for parts 1 - 10000] is already closed
2022-04-19 13:13:19 destination > 2022-04-19 13:13:19 INFO a.m.s.StreamTransferManager(complete):367 - [Manager uploading to travlrdatatesting/airbyte_data/brands/2022_04_19_1650365489228_9dc06c5f-d8a7-4901-b41d-a9c8e16df070.csv with id zgP5ZcAsh...ojN7UxA--]: Uploading leftover stream [Part number 1 containing 0.02 MB]
2022-04-19 13:13:20 destination > 2022-04-19 13:13:20 INFO a.m.s.StreamTransferManager(uploadStreamPart):558 - [Manager uploading to travlrdatatesting/airbyte_data/brands/2022_04_19_1650365489228_9dc06c5f-d8a7-4901-b41d-a9c8e16df070.csv with id zgP5ZcAsh...ojN7UxA--]: Finished uploading [Part number 1 containing 0.02 MB]
2022-04-19 13:13:20 destination > 2022-04-19 13:13:20 INFO a.m.s.StreamTransferManager(complete):395 - [Manager uploading to travlrdatatesting/airbyte_data/brands/2022_04_19_1650365489228_9dc06c5f-d8a7-4901-b41d-a9c8e16df070.csv with id zgP5ZcAsh...ojN7UxA--]: Completed
2022-04-19 13:13:20 destination > 2022-04-19 13:13:20 INFO i.a.i.d.s.w.BaseS3Writer(close):115 - Upload completed for stream 'brands'.
2022-04-19 13:13:20 destination > 2022-04-19 13:13:20 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationSchema):152 - Creating schema in destination if it doesn't exist: zendesk
2022-04-19 13:13:26 destination > 2022-04-19 13:13:26 INFO i.a.i.d.j.c.s.S3StreamCopier(createTemporaryTable):158 - Preparing tmp table in destination for stream: brands, schema: zendesk, tmp table name: _airbyte_tmp_unr_brands.
2022-04-19 13:13:27 destination > 2022-04-19 13:13:27 INFO i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):85 - Starting copy to tmp table: _airbyte_tmp_unr_brands in destination for stream: brands, schema: zendesk, .
2022-04-19 13:13:29 destination > 2022-04-19 13:13:29 INFO i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):89 - Copy to tmp table _airbyte_tmp_unr_brands in destination for stream brands complete.
2022-04-19 13:13:29 destination > 2022-04-19 13:13:29 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):174 - Preparing table _airbyte_raw_brands in destination.
2022-04-19 13:13:29 destination > 2022-04-19 13:13:29 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):176 - Table _airbyte_tmp_unr_brands in destination prepared.
2022-04-19 13:13:29 destination > 2022-04-19 13:13:29 INFO i.a.i.d.j.c.s.S3StreamCopier(generateMergeStatement):183 - Preparing to merge tmp table _airbyte_tmp_unr_brands to dest table: _airbyte_raw_brands, schema: zendesk, in destination.
2022-04-19 13:13:29 destination > 2022-04-19 13:13:29 INFO i.a.i.d.s.w.BaseS3Writer(close):113 - Uploading remaining data for stream 'sla_policies'.
2022-04-19 13:13:29 destination > 2022-04-19 13:13:29 INFO a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-19 13:13:29 destination > 2022-04-19 13:13:29 INFO a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-19 13:13:29 destination > 2022-04-19 13:13:29 WARN a.m.s.MultiPartOutputStream(close):160 - [MultipartOutputStream for parts 1 - 10000] is already closed
2022-04-19 13:13:29 destination > 2022-04-19 13:13:29 INFO a.m.s.StreamTransferManager(complete):367 - [Manager uploading to travlrdatatesting/airbyte_data/sla_policies/2022_04_19_1650365489230_b4ec8c47-505c-4324-b71d-383b700bcbc5.csv with id G0Ap2L3wi...rP7HD2A--]: Uploading leftover stream [Part number 1 containing 0.03 MB]
2022-04-19 13:13:29 destination > 2022-04-19 13:13:29 INFO a.m.s.StreamTransferManager(uploadStreamPart):558 - [Manager uploading to travlrdatatesting/airbyte_data/sla_policies/2022_04_19_1650365489230_b4ec8c47-505c-4324-b71d-383b700bcbc5.csv with id G0Ap2L3wi...rP7HD2A--]: Finished uploading [Part number 1 containing 0.03 MB]
2022-04-19 13:13:29 destination > 2022-04-19 13:13:29 INFO a.m.s.StreamTransferManager(complete):395 - [Manager uploading to travlrdatatesting/airbyte_data/sla_policies/2022_04_19_1650365489230_b4ec8c47-505c-4324-b71d-383b700bcbc5.csv with id G0Ap2L3wi...rP7HD2A--]: Completed
2022-04-19 13:13:29 destination > 2022-04-19 13:13:29 INFO i.a.i.d.s.w.BaseS3Writer(close):115 - Upload completed for stream 'sla_policies'.
2022-04-19 13:13:29 destination > 2022-04-19 13:13:29 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationSchema):152 - Creating schema in destination if it doesn't exist: zendesk
2022-04-19 13:13:30 destination > 2022-04-19 13:13:30 INFO i.a.i.d.j.c.s.S3StreamCopier(createTemporaryTable):158 - Preparing tmp table in destination for stream: sla_policies, schema: zendesk, tmp table name: _airbyte_tmp_haq_sla_policies.
2022-04-19 13:13:30 destination > 2022-04-19 13:13:30 INFO i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):85 - Starting copy to tmp table: _airbyte_tmp_haq_sla_policies in destination for stream: sla_policies, schema: zendesk, .
2022-04-19 13:13:31 destination > 2022-04-19 13:13:31 INFO i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):89 - Copy to tmp table _airbyte_tmp_haq_sla_policies in destination for stream sla_policies complete.
2022-04-19 13:13:31 destination > 2022-04-19 13:13:31 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):174 - Preparing table _airbyte_raw_sla_policies in destination.
2022-04-19 13:13:31 destination > 2022-04-19 13:13:31 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):176 - Table _airbyte_tmp_haq_sla_policies in destination prepared.
2022-04-19 13:13:31 destination > 2022-04-19 13:13:31 INFO i.a.i.d.j.c.s.S3StreamCopier(generateMergeStatement):183 - Preparing to merge tmp table _airbyte_tmp_haq_sla_policies to dest table: _airbyte_raw_sla_policies, schema: zendesk, in destination.
2022-04-19 13:13:31 destination > 2022-04-19 13:13:31 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationSchema):152 - Creating schema in destination if it doesn't exist: zendesk
2022-04-19 13:13:31 destination > 2022-04-19 13:13:31 INFO i.a.i.d.j.c.s.S3StreamCopier(createTemporaryTable):158 - Preparing tmp table in destination for stream: tickets, schema: zendesk, tmp table name: _airbyte_tmp_rsk_tickets.
2022-04-19 13:13:31 destination > 2022-04-19 13:13:31 INFO i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):85 - Starting copy to tmp table: _airbyte_tmp_rsk_tickets in destination for stream: tickets, schema: zendesk, .
2022-04-19 13:13:31 destination > 2022-04-19 13:13:31 INFO i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):89 - Copy to tmp table _airbyte_tmp_rsk_tickets in destination for stream tickets complete.
2022-04-19 13:13:31 destination > 2022-04-19 13:13:31 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):174 - Preparing table _airbyte_raw_tickets in destination.
2022-04-19 13:13:31 destination > 2022-04-19 13:13:31 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):176 - Table _airbyte_tmp_rsk_tickets in destination prepared.
2022-04-19 13:13:31 destination > 2022-04-19 13:13:31 INFO i.a.i.d.j.c.s.S3StreamCopier(generateMergeStatement):183 - Preparing to merge tmp table _airbyte_tmp_rsk_tickets to dest table: _airbyte_raw_tickets, schema: zendesk, in destination.
2022-04-19 13:13:31 destination > 2022-04-19 13:13:31 INFO i.a.i.d.s.w.BaseS3Writer(close):113 - Uploading remaining data for stream 'ticket_metric_events'.
2022-04-19 13:13:31 destination > 2022-04-19 13:13:31 INFO a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-19 13:13:31 destination > 2022-04-19 13:13:31 INFO a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-19 13:13:31 destination > 2022-04-19 13:13:31 WARN a.m.s.MultiPartOutputStream(close):160 - [MultipartOutputStream for parts 1 - 10000] is already closed
2022-04-19 13:13:31 destination > 2022-04-19 13:13:31 INFO a.m.s.StreamTransferManager(complete):367 - [Manager uploading to travlrdatatesting/airbyte_data/ticket_metric_events/2022_04_19_1650365489231_dec743cb-f6b0-4fac-8244-152444fd5a6d.csv with id 2RaWFw_1R...WC2H2Jg--]: Uploading leftover stream [Part number 1 containing 1.34 MB]
2022-04-19 13:13:33 destination > 2022-04-19 13:13:33 INFO a.m.s.StreamTransferManager(uploadStreamPart):558 - [Manager uploading to travlrdatatesting/airbyte_data/ticket_metric_events/2022_04_19_1650365489231_dec743cb-f6b0-4fac-8244-152444fd5a6d.csv with id 2RaWFw_1R...WC2H2Jg--]: Finished uploading [Part number 1 containing 1.34 MB]
2022-04-19 13:13:33 destination > 2022-04-19 13:13:33 INFO a.m.s.StreamTransferManager(complete):395 - [Manager uploading to travlrdatatesting/airbyte_data/ticket_metric_events/2022_04_19_1650365489231_dec743cb-f6b0-4fac-8244-152444fd5a6d.csv with id 2RaWFw_1R...WC2H2Jg--]: Completed
2022-04-19 13:13:33 destination > 2022-04-19 13:13:33 INFO i.a.i.d.s.w.BaseS3Writer(close):115 - Upload completed for stream 'ticket_metric_events'.
2022-04-19 13:13:33 destination > 2022-04-19 13:13:33 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationSchema):152 - Creating schema in destination if it doesn't exist: zendesk
2022-04-19 13:13:33 destination > 2022-04-19 13:13:33 INFO i.a.i.d.j.c.s.S3StreamCopier(createTemporaryTable):158 - Preparing tmp table in destination for stream: ticket_metric_events, schema: zendesk, tmp table name: _airbyte_tmp_zmx_ticket_metric_events.
2022-04-19 13:13:33 destination > 2022-04-19 13:13:33 INFO i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):85 - Starting copy to tmp table: _airbyte_tmp_zmx_ticket_metric_events in destination for stream: ticket_metric_events, schema: zendesk, .
2022-04-19 13:13:34 destination > 2022-04-19 13:13:34 INFO i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):89 - Copy to tmp table _airbyte_tmp_zmx_ticket_metric_events in destination for stream ticket_metric_events complete.
2022-04-19 13:13:34 destination > 2022-04-19 13:13:34 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):174 - Preparing table _airbyte_raw_ticket_metric_events in destination.
2022-04-19 13:13:34 destination > 2022-04-19 13:13:34 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):176 - Table _airbyte_tmp_zmx_ticket_metric_events in destination prepared.
2022-04-19 13:13:34 destination > 2022-04-19 13:13:34 INFO i.a.i.d.j.c.s.S3StreamCopier(generateMergeStatement):183 - Preparing to merge tmp table _airbyte_tmp_zmx_ticket_metric_events to dest table: _airbyte_raw_ticket_metric_events, schema: zendesk, in destination.
2022-04-19 13:13:34 destination > 2022-04-19 13:13:34 INFO i.a.i.d.s.w.BaseS3Writer(close):113 - Uploading remaining data for stream 'ticket_metrics'.
2022-04-19 13:13:34 destination > 2022-04-19 13:13:34 INFO a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-19 13:13:34 destination > 2022-04-19 13:13:34 INFO a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-19 13:13:34 destination > 2022-04-19 13:13:34 WARN a.m.s.MultiPartOutputStream(close):160 - [MultipartOutputStream for parts 1 - 10000] is already closed
2022-04-19 13:13:38 destination > 2022-04-19 13:13:38 INFO a.m.s.StreamTransferManager(uploadStreamPart):558 - [Manager uploading to travlrdatatesting/airbyte_data/ticket_metrics/2022_04_19_1650365489231_3834c0e3-6f70-4716-9b22-01ea5c83901e.csv with id FDfzzvyL8...FBbgxnw--]: Finished uploading [Part number 1 containing 14.99 MB]
2022-04-19 13:13:38 destination > 2022-04-19 13:13:38 INFO a.m.s.StreamTransferManager(complete):395 - [Manager uploading to travlrdatatesting/airbyte_data/ticket_metrics/2022_04_19_1650365489231_3834c0e3-6f70-4716-9b22-01ea5c83901e.csv with id FDfzzvyL8...FBbgxnw--]: Completed
2022-04-19 13:13:38 destination > 2022-04-19 13:13:38 INFO i.a.i.d.s.w.BaseS3Writer(close):115 - Upload completed for stream 'ticket_metrics'.
2022-04-19 13:13:38 destination > 2022-04-19 13:13:38 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationSchema):152 - Creating schema in destination if it doesn't exist: zendesk
2022-04-19 13:13:39 destination > 2022-04-19 13:13:39 INFO i.a.i.d.j.c.s.S3StreamCopier(createTemporaryTable):158 - Preparing tmp table in destination for stream: ticket_metrics, schema: zendesk, tmp table name: _airbyte_tmp_zfr_ticket_metrics.
2022-04-19 13:13:39 destination > 2022-04-19 13:13:39 INFO i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):85 - Starting copy to tmp table: _airbyte_tmp_zfr_ticket_metrics in destination for stream: ticket_metrics, schema: zendesk, .
2022-04-19 13:13:40 destination > 2022-04-19 13:13:40 INFO i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):89 - Copy to tmp table _airbyte_tmp_zfr_ticket_metrics in destination for stream ticket_metrics complete.
2022-04-19 13:13:40 destination > 2022-04-19 13:13:40 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):174 - Preparing table _airbyte_raw_ticket_metrics in destination.
2022-04-19 13:13:41 destination > 2022-04-19 13:13:41 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):176 - Table _airbyte_tmp_zfr_ticket_metrics in destination prepared.
2022-04-19 13:13:41 destination > 2022-04-19 13:13:41 INFO i.a.i.d.j.c.s.S3StreamCopier(generateMergeStatement):183 - Preparing to merge tmp table _airbyte_tmp_zfr_ticket_metrics to dest table: _airbyte_raw_ticket_metrics, schema: zendesk, in destination.
2022-04-19 13:13:41 destination > 2022-04-19 13:13:41 INFO i.a.i.d.s.w.BaseS3Writer(close):113 - Uploading remaining data for stream 'tags'.
2022-04-19 13:13:41 destination > 2022-04-19 13:13:41 INFO a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-19 13:13:41 destination > 2022-04-19 13:13:41 INFO a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-19 13:13:41 destination > 2022-04-19 13:13:41 WARN a.m.s.MultiPartOutputStream(close):160 - [MultipartOutputStream for parts 1 - 10000] is already closed
2022-04-19 13:13:41 destination > 2022-04-19 13:13:41 INFO a.m.s.StreamTransferManager(complete):367 - [Manager uploading to travlrdatatesting/airbyte_data/tags/2022_04_19_1650365489230_ddd5b656-ef4f-4aa1-90e1-9b4e351289a9.csv with id sI6BWXhW1...hYibb5g--]: Uploading leftover stream [Part number 1 containing 0.02 MB]
2022-04-19 13:13:41 destination > 2022-04-19 13:13:41 INFO a.m.s.StreamTransferManager(uploadStreamPart):558 - [Manager uploading to travlrdatatesting/airbyte_data/tags/2022_04_19_1650365489230_ddd5b656-ef4f-4aa1-90e1-9b4e351289a9.csv with id sI6BWXhW1...hYibb5g--]: Finished uploading [Part number 1 containing 0.02 MB]
2022-04-19 13:13:41 destination > 2022-04-19 13:13:41 INFO a.m.s.StreamTransferManager(complete):395 - [Manager uploading to travlrdatatesting/airbyte_data/tags/2022_04_19_1650365489230_ddd5b656-ef4f-4aa1-90e1-9b4e351289a9.csv with id sI6BWXhW1...hYibb5g--]: Completed
2022-04-19 13:13:41 destination > 2022-04-19 13:13:41 INFO i.a.i.d.s.w.BaseS3Writer(close):115 - Upload completed for stream 'tags'.
2022-04-19 13:13:41 destination > 2022-04-19 13:13:41 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationSchema):152 - Creating schema in destination if it doesn't exist: zendesk
2022-04-19 13:13:41 destination > 2022-04-19 13:13:41 INFO i.a.i.d.j.c.s.S3StreamCopier(createTemporaryTable):158 - Preparing tmp table in destination for stream: tags, schema: zendesk, tmp table name: _airbyte_tmp_lrs_tags.
2022-04-19 13:13:41 destination > 2022-04-19 13:13:41 INFO i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):85 - Starting copy to tmp table: _airbyte_tmp_lrs_tags in destination for stream: tags, schema: zendesk, .
2022-04-19 13:13:42 destination > 2022-04-19 13:13:42 INFO i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):89 - Copy to tmp table _airbyte_tmp_lrs_tags in destination for stream tags complete.
2022-04-19 13:13:42 destination > 2022-04-19 13:13:42 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):174 - Preparing table _airbyte_raw_tags in destination.
2022-04-19 13:13:42 destination > 2022-04-19 13:13:42 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):176 - Table _airbyte_tmp_lrs_tags in destination prepared.
2022-04-19 13:13:42 destination > 2022-04-19 13:13:42 INFO i.a.i.d.j.c.s.S3StreamCopier(generateMergeStatement):183 - Preparing to merge tmp table _airbyte_tmp_lrs_tags to dest table: _airbyte_raw_tags, schema: zendesk, in destination.
2022-04-19 13:13:42 destination > 2022-04-19 13:13:42 INFO i.a.i.d.s.w.BaseS3Writer(close):113 - Uploading remaining data for stream 'ticket_fields'.
2022-04-19 13:13:42 destination > 2022-04-19 13:13:42 INFO a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-19 13:13:42 destination > 2022-04-19 13:13:42 INFO a.m.s.MultiPartOutputStream(close):158 - Called close() on [MultipartOutputStream for parts 1 - 10000]
2022-04-19 13:13:42 destination > 2022-04-19 13:13:42 WARN a.m.s.MultiPartOutputStream(close):160 - [MultipartOutputStream for parts 1 - 10000] is already closed
2022-04-19 13:13:42 destination > 2022-04-19 13:13:42 INFO a.m.s.StreamTransferManager(complete):367 - [Manager uploading to travlrdatatesting/airbyte_data/ticket_fields/2022_04_19_1650365489230_b6d71c3d-1fd6-4c31-8899-d726045b84f2.csv with id RnquPRcCj...h7PMoHQ--]: Uploading leftover stream [Part number 1 containing 0.02 MB]
2022-04-19 13:13:42 destination > 2022-04-19 13:13:42 INFO a.m.s.StreamTransferManager(uploadStreamPart):558 - [Manager uploading to travlrdatatesting/airbyte_data/ticket_fields/2022_04_19_1650365489230_b6d71c3d-1fd6-4c31-8899-d726045b84f2.csv with id RnquPRcCj...h7PMoHQ--]: Finished uploading [Part number 1 containing 0.02 MB]
2022-04-19 13:13:42 destination > 2022-04-19 13:13:42 INFO a.m.s.StreamTransferManager(complete):395 - [Manager uploading to travlrdatatesting/airbyte_data/ticket_fields/2022_04_19_1650365489230_b6d71c3d-1fd6-4c31-8899-d726045b84f2.csv with id RnquPRcCj...h7PMoHQ--]: Completed
2022-04-19 13:13:42 destination > 2022-04-19 13:13:42 INFO i.a.i.d.s.w.BaseS3Writer(close):115 - Upload completed for stream 'ticket_fields'.
2022-04-19 13:13:42 destination > 2022-04-19 13:13:42 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationSchema):152 - Creating schema in destination if it doesn't exist: zendesk
2022-04-19 13:13:42 destination > 2022-04-19 13:13:42 INFO i.a.i.d.j.c.s.S3StreamCopier(createTemporaryTable):158 - Preparing tmp table in destination for stream: ticket_fields, schema: zendesk, tmp table name: _airbyte_tmp_oxl_ticket_fields.
2022-04-19 13:13:43 destination > 2022-04-19 13:13:43 INFO i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):85 - Starting copy to tmp table: _airbyte_tmp_oxl_ticket_fields in destination for stream: ticket_fields, schema: zendesk, .
2022-04-19 13:13:43 destination > 2022-04-19 13:13:43 INFO i.a.i.d.r.RedshiftStreamCopier(copyStagingFileToTemporaryTable):89 - Copy to tmp table _airbyte_tmp_oxl_ticket_fields in destination for stream ticket_fields complete.
2022-04-19 13:13:43 destination > 2022-04-19 13:13:43 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):174 - Preparing table _airbyte_raw_ticket_fields in destination.
2022-04-19 13:13:43 destination > 2022-04-19 13:13:43 INFO i.a.i.d.j.c.s.S3StreamCopier(createDestinationTable):176 - Table _airbyte_tmp_oxl_ticket_fields in destination prepared.
2022-04-19 13:13:43 destination > 2022-04-19 13:13:43 INFO i.a.i.d.j.c.s.S3StreamCopier(generateMergeStatement):183 - Preparing to merge tmp table _airbyte_tmp_oxl_ticket_fields to dest table: _airbyte_raw_ticket_fields, schema: zendesk, in destination.
2022-04-19 13:13:44 destination > 2022-04-19 13:13:44 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):198 - S3 staging file airbyte_data/brands/2022_04_19_1650365489228_9dc06c5f-d8a7-4901-b41d-a9c8e16df070.csv cleaned.
2022-04-19 13:13:44 destination > 2022-04-19 13:13:44 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):202 - Begin cleaning _airbyte_tmp_unr_brands tmp table in destination.
2022-04-19 13:13:44 destination > 2022-04-19 13:13:44 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):204 - _airbyte_tmp_unr_brands tmp table in destination cleaned.
2022-04-19 13:13:44 destination > 2022-04-19 13:13:44 INFO i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):105 - Begin cleaning s3 manifest file airbyte_data/758fcf15-a12e-4e82-b8f6-d894cfaee5c9/zendesk/d2356b62-9166-4642-82f0-9c2e50e266b5.manifest.
2022-04-19 13:13:44 destination > 2022-04-19 13:13:44 INFO i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):109 - S3 manifest file airbyte_data/758fcf15-a12e-4e82-b8f6-d894cfaee5c9/zendesk/d2356b62-9166-4642-82f0-9c2e50e266b5.manifest cleaned.
2022-04-19 13:13:44 destination > 2022-04-19 13:13:44 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):198 - S3 staging file airbyte_data/sla_policies/2022_04_19_1650365489230_b4ec8c47-505c-4324-b71d-383b700bcbc5.csv cleaned.
2022-04-19 13:13:44 destination > 2022-04-19 13:13:44 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):202 - Begin cleaning _airbyte_tmp_haq_sla_policies tmp table in destination.
2022-04-19 13:13:44 destination > 2022-04-19 13:13:44 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):204 - _airbyte_tmp_haq_sla_policies tmp table in destination cleaned.
2022-04-19 13:13:44 destination > 2022-04-19 13:13:44 INFO i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):105 - Begin cleaning s3 manifest file airbyte_data/758fcf15-a12e-4e82-b8f6-d894cfaee5c9/zendesk/76b3fa39-e730-41d6-ab89-7ea75d1f462b.manifest.
2022-04-19 13:13:45 destination > 2022-04-19 13:13:45 INFO i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):109 - S3 manifest file airbyte_data/758fcf15-a12e-4e82-b8f6-d894cfaee5c9/zendesk/76b3fa39-e730-41d6-ab89-7ea75d1f462b.manifest cleaned.
2022-04-19 13:13:45 destination > 2022-04-19 13:13:45 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):202 - Begin cleaning _airbyte_tmp_rsk_tickets tmp table in destination.
2022-04-19 13:13:45 destination > 2022-04-19 13:13:45 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):204 - _airbyte_tmp_rsk_tickets tmp table in destination cleaned.
2022-04-19 13:13:45 destination > 2022-04-19 13:13:45 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):198 - S3 staging file airbyte_data/ticket_metric_events/2022_04_19_1650365489231_dec743cb-f6b0-4fac-8244-152444fd5a6d.csv cleaned.
2022-04-19 13:13:45 destination > 2022-04-19 13:13:45 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):202 - Begin cleaning _airbyte_tmp_zmx_ticket_metric_events tmp table in destination.
2022-04-19 13:13:45 destination > 2022-04-19 13:13:45 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):204 - _airbyte_tmp_zmx_ticket_metric_events tmp table in destination cleaned.
2022-04-19 13:13:45 destination > 2022-04-19 13:13:45 INFO i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):105 - Begin cleaning s3 manifest file airbyte_data/758fcf15-a12e-4e82-b8f6-d894cfaee5c9/zendesk/f4e6d3a5-255a-4c87-85dd-931183e9177b.manifest.
2022-04-19 13:13:45 destination > 2022-04-19 13:13:45 INFO i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):109 - S3 manifest file airbyte_data/758fcf15-a12e-4e82-b8f6-d894cfaee5c9/zendesk/f4e6d3a5-255a-4c87-85dd-931183e9177b.manifest cleaned.
2022-04-19 13:13:45 destination > 2022-04-19 13:13:45 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):198 - S3 staging file airbyte_data/ticket_metrics/2022_04_19_1650365489231_3834c0e3-6f70-4716-9b22-01ea5c83901e.csv cleaned.
2022-04-19 13:13:45 destination > 2022-04-19 13:13:45 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):202 - Begin cleaning _airbyte_tmp_zfr_ticket_metrics tmp table in destination.
2022-04-19 13:13:45 destination > 2022-04-19 13:13:45 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):204 - _airbyte_tmp_zfr_ticket_metrics tmp table in destination cleaned.
2022-04-19 13:13:45 destination > 2022-04-19 13:13:45 INFO i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):105 - Begin cleaning s3 manifest file airbyte_data/758fcf15-a12e-4e82-b8f6-d894cfaee5c9/zendesk/fb835c54-6404-44b6-8763-78ed81bc04fe.manifest.
2022-04-19 13:13:45 destination > 2022-04-19 13:13:45 INFO i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):109 - S3 manifest file airbyte_data/758fcf15-a12e-4e82-b8f6-d894cfaee5c9/zendesk/fb835c54-6404-44b6-8763-78ed81bc04fe.manifest cleaned.
2022-04-19 13:13:46 destination > 2022-04-19 13:13:46 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):198 - S3 staging file airbyte_data/tags/2022_04_19_1650365489230_ddd5b656-ef4f-4aa1-90e1-9b4e351289a9.csv cleaned.
2022-04-19 13:13:46 destination > 2022-04-19 13:13:46 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):202 - Begin cleaning _airbyte_tmp_lrs_tags tmp table in destination.
2022-04-19 13:13:46 destination > 2022-04-19 13:13:46 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):204 - _airbyte_tmp_lrs_tags tmp table in destination cleaned.
2022-04-19 13:13:46 destination > 2022-04-19 13:13:46 INFO i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):105 - Begin cleaning s3 manifest file airbyte_data/758fcf15-a12e-4e82-b8f6-d894cfaee5c9/zendesk/89d43e57-c273-497e-82e4-a3d145f109e9.manifest.
2022-04-19 13:13:46 destination > 2022-04-19 13:13:46 INFO i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):109 - S3 manifest file airbyte_data/758fcf15-a12e-4e82-b8f6-d894cfaee5c9/zendesk/89d43e57-c273-497e-82e4-a3d145f109e9.manifest cleaned.
2022-04-19 13:13:46 destination > 2022-04-19 13:13:46 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):198 - S3 staging file airbyte_data/ticket_fields/2022_04_19_1650365489230_b6d71c3d-1fd6-4c31-8899-d726045b84f2.csv cleaned.
2022-04-19 13:13:46 destination > 2022-04-19 13:13:46 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):202 - Begin cleaning _airbyte_tmp_oxl_ticket_fields tmp table in destination.
2022-04-19 13:13:46 destination > 2022-04-19 13:13:46 INFO i.a.i.d.j.c.s.S3StreamCopier(removeFileAndDropTmpTable):204 - _airbyte_tmp_oxl_ticket_fields tmp table in destination cleaned.
2022-04-19 13:13:46 destination > 2022-04-19 13:13:46 INFO i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):105 - Begin cleaning s3 manifest file airbyte_data/758fcf15-a12e-4e82-b8f6-d894cfaee5c9/zendesk/e41aebcd-46e7-4110-9fba-bd7a11e497ab.manifest.
2022-04-19 13:13:46 destination > 2022-04-19 13:13:46 INFO i.a.i.d.r.RedshiftStreamCopier(removeFileAndDropTmpTable):109 - S3 manifest file airbyte_data/758fcf15-a12e-4e82-b8f6-d894cfaee5c9/zendesk/e41aebcd-46e7-4110-9fba-bd7a11e497ab.manifest cleaned.
2022-04-19 13:13:46 INFO i.a.w.DefaultReplicationWorker(lambda$getDestinationOutputRunnable$6):347 - State in DefaultReplicationWorker from destination: io.airbyte.protocol.models.AirbyteMessage@68c52ab5[type=STATE,log=<null>,spec=<null>,connectionStatus=<null>,catalog=<null>,record=<null>,state=io.airbyte.protocol.models.AirbyteStateMessage@6602d9dd[data={"ticket_fields":{"updated_at":"2022-04-14T06:20:39Z"},"ticket_metric_events":{"time":"2022-04-19T10:29:34Z"}},additionalProperties={}],additionalProperties={}]
2022-04-19 13:13:46 destination > 2022-04-19 13:13:46 INFO i.a.i.b.IntegrationRunner(runInternal):169 - Completed integration: io.airbyte.integrations.destination.redshift.RedshiftDestination
2022-04-19 13:13:46 destination > 2022-04-19 13:13:46 INFO i.a.i.d.r.RedshiftDestination(main):79 - completed destination: class io.airbyte.integrations.destination.redshift.RedshiftDestination
2022-04-19 13:13:47 ERROR i.a.w.DefaultReplicationWorker(run):168 - Sync worker failed.
java.util.concurrent.ExecutionException: io.airbyte.workers.DefaultReplicationWorker$SourceException: Source process exited with non-zero exit code 1
	at java.util.concurrent.CompletableFuture.reportGet(CompletableFuture.java:396) ~[?:?]
	at java.util.concurrent.CompletableFuture.get(CompletableFuture.java:2073) ~[?:?]
	at io.airbyte.workers.DefaultReplicationWorker.run(DefaultReplicationWorker.java:161) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.DefaultReplicationWorker.run(DefaultReplicationWorker.java:56) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.temporal.TemporalAttemptExecution.lambda$getWorkerThread$2(TemporalAttemptExecution.java:155) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at java.lang.Thread.run(Thread.java:833) [?:?]
	Suppressed: io.airbyte.workers.WorkerException: Source process exit with code 1. This warning is normal if the job was cancelled.
		at io.airbyte.workers.protocols.airbyte.DefaultAirbyteSource.close(DefaultAirbyteSource.java:136) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at io.airbyte.workers.DefaultReplicationWorker.run(DefaultReplicationWorker.java:125) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at io.airbyte.workers.DefaultReplicationWorker.run(DefaultReplicationWorker.java:56) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at io.airbyte.workers.temporal.TemporalAttemptExecution.lambda$getWorkerThread$2(TemporalAttemptExecution.java:155) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at java.lang.Thread.run(Thread.java:833) [?:?]
Caused by: io.airbyte.workers.DefaultReplicationWorker$SourceException: Source process exited with non-zero exit code 1
	at io.airbyte.workers.DefaultReplicationWorker.lambda$getReplicationRunnable$5(DefaultReplicationWorker.java:310) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at java.util.concurrent.CompletableFuture$AsyncRun.run(CompletableFuture.java:1804) ~[?:?]
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136) ~[?:?]
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635) ~[?:?]
	... 1 more
2022-04-19 13:13:47 INFO i.a.w.DefaultReplicationWorker(run):227 - sync summary: io.airbyte.config.ReplicationAttemptSummary@18c6d0a5[status=failed,recordsSynced=21093,bytesSynced=14420657,startTime=1650365483927,endTime=1650374027867,totalStats=io.airbyte.config.SyncStats@46d61d88[recordsEmitted=21093,bytesEmitted=14420657,stateMessagesEmitted=2,recordsCommitted=6317],streamStats=[io.airbyte.config.StreamSyncStats@3dabbe5[streamName=ticket_fields,stats=io.airbyte.config.SyncStats@681618c7[recordsEmitted=14,bytesEmitted=14758,stateMessagesEmitted=<null>,recordsCommitted=14]], io.airbyte.config.StreamSyncStats@467bbe98[streamName=brands,stats=io.airbyte.config.SyncStats@3037c182[recordsEmitted=12,bytesEmitted=20589,stateMessagesEmitted=<null>,recordsCommitted=12]], io.airbyte.config.StreamSyncStats@666441f2[streamName=ticket_metrics,stats=io.airbyte.config.SyncStats@3eecadd9[recordsEmitted=14776,bytesEmitted=13462693,stateMessagesEmitted=<null>,recordsCommitted=<null>]], io.airbyte.config.StreamSyncStats@25784be2[streamName=sla_policies,stats=io.airbyte.config.SyncStats@2b4db51e[recordsEmitted=11,bytesEmitted=26555,stateMessagesEmitted=<null>,recordsCommitted=11]], io.airbyte.config.StreamSyncStats@6fe21a54[streamName=ticket_metric_events,stats=io.airbyte.config.SyncStats@34bbf014[recordsEmitted=6084,bytesEmitted=889997,stateMessagesEmitted=<null>,recordsCommitted=6084]], io.airbyte.config.StreamSyncStats@53fb9a3a[streamName=tags,stats=io.airbyte.config.SyncStats@15d8201e[recordsEmitted=196,bytesEmitted=6065,stateMessagesEmitted=<null>,recordsCommitted=196]]]]
2022-04-19 13:13:47 INFO i.a.w.DefaultReplicationWorker(run):247 - Source output at least one state message
2022-04-19 13:13:47 INFO i.a.w.DefaultReplicationWorker(run):253 - State capture: Updated state to: Optional[io.airbyte.config.State@6a97a84b[state={"ticket_fields":{"updated_at":"2022-04-14T06:20:39Z"},"ticket_metric_events":{"time":"2022-04-19T10:29:34Z"}}]]
2022-04-19 13:13:47 INFO i.a.w.t.TemporalAttemptExecution(get):131 - Stopping cancellation check scheduling...
2022-04-19 13:13:47 INFO i.a.w.t.s.ReplicationActivityImpl(lambda$replicate$1):144 - sync summary: io.airbyte.config.StandardSyncOutput@30481e05[standardSyncSummary=io.airbyte.config.StandardSyncSummary@3f81b13c[status=failed,recordsSynced=21093,bytesSynced=14420657,startTime=1650365483927,endTime=1650374027867,totalStats=io.airbyte.config.SyncStats@46d61d88[recordsEmitted=21093,bytesEmitted=14420657,stateMessagesEmitted=2,recordsCommitted=6317],streamStats=[io.airbyte.config.StreamSyncStats@3dabbe5[streamName=ticket_fields,stats=io.airbyte.config.SyncStats@681618c7[recordsEmitted=14,bytesEmitted=14758,stateMessagesEmitted=<null>,recordsCommitted=14]], io.airbyte.config.StreamSyncStats@467bbe98[streamName=brands,stats=io.airbyte.config.SyncStats@3037c182[recordsEmitted=12,bytesEmitted=20589,stateMessagesEmitted=<null>,recordsCommitted=12]], io.airbyte.config.StreamSyncStats@666441f2[streamName=ticket_metrics,stats=io.airbyte.config.SyncStats@3eecadd9[recordsEmitted=14776,bytesEmitted=13462693,stateMessagesEmitted=<null>,recordsCommitted=<null>]], io.airbyte.config.StreamSyncStats@25784be2[streamName=sla_policies,stats=io.airbyte.config.SyncStats@2b4db51e[recordsEmitted=11,bytesEmitted=26555,stateMessagesEmitted=<null>,recordsCommitted=11]], io.airbyte.config.StreamSyncStats@6fe21a54[streamName=ticket_metric_events,stats=io.airbyte.config.SyncStats@34bbf014[recordsEmitted=6084,bytesEmitted=889997,stateMessagesEmitted=<null>,recordsCommitted=6084]], io.airbyte.config.StreamSyncStats@53fb9a3a[streamName=tags,stats=io.airbyte.config.SyncStats@15d8201e[recordsEmitted=196,bytesEmitted=6065,stateMessagesEmitted=<null>,recordsCommitted=196]]]],state=io.airbyte.config.State@6a97a84b[state={"ticket_fields":{"updated_at":"2022-04-14T06:20:39Z"},"ticket_metric_events":{"time":"2022-04-19T10:29:34Z"}}],outputCatalog=io.airbyte.protocol.models.ConfiguredAirbyteCatalog@48e6ec6e[streams=[io.airbyte.protocol.models.ConfiguredAirbyteStream@6eeae2ab[stream=io.airbyte.protocol.models.AirbyteStream@1e227145[name=brands,jsonSchema={"type":["null","object"],"properties":{"id":{"type":["null","integer"]},"url":{"type":["null","string"]},"logo":{"type":["null","string"]},"name":{"type":["null","string"]},"active":{"type":["null","boolean"]},"default":{"type":["null","boolean"]},"brand_url":{"type":["null","string"]},"subdomain":{"type":["null","string"]},"created_at":{"type":["null","string"],"format":"date-time"},"is_deleted":{"type":["null","boolean"]},"updated_at":{"type":["null","string"],"format":"date-time"},"host_mapping":{"type":["null","string"]},"has_help_center":{"type":["null","boolean"]},"ticket_form_ids":{"type":["null","array"]},"help_center_state":{"type":["null","string"]},"signature_template":{"type":["null","string"]}}},supportedSyncModes=[full_refresh],sourceDefinedCursor=<null>,defaultCursorField=[],sourceDefinedPrimaryKey=[[id]],namespace=<null>,additionalProperties={}],syncMode=full_refresh,cursorField=[],destinationSyncMode=append,primaryKey=[[id]],additionalProperties={}], io.airbyte.protocol.models.ConfiguredAirbyteStream@2c983bec[stream=io.airbyte.protocol.models.AirbyteStream@53e2278d[name=sla_policies,jsonSchema={"type":["object"],"properties":{"id":{"type":["integer"]},"url":{"type":["null","string"]},"title":{"type":["null","string"]},"filter":{"type":["null","object"],"properties":{"all":{"type":["null","array"],"items":{"type":["object"],"properties":{"field":{"type":["null","string"]},"value":{"type":["null","string","number","boolean"]},"operator":{"type":["null","string"]}}}},"any":{"type":["null","array"],"items":{"type":["object"],"properties":{"field":{"type":["null","string"]},"value":{"type":["null","string"]},"operator":{"type":["null","string"]}}}}}},"position":{"type":["null","integer"]},"created_at":{"type":["null","string"],"format":"date-time"},"updated_at":{"type":["null","string"],"format":"date-time"},"description":{"type":["null","string"]},"policy_metrics":{"type":["null","array"],"items":{"type":["null","object"],"properties":{"metric":{"type":["null","string"]},"target":{"type":["null","integer"]},"priority":{"type":["null","string"]},"business_hours":{"type":["null","boolean"]}}}}}},supportedSyncModes=[full_refresh],sourceDefinedCursor=<null>,defaultCursorField=[],sourceDefinedPrimaryKey=[[id]],namespace=<null>,additionalProperties={}],syncMode=full_refresh,cursorField=[],destinationSyncMode=append,primaryKey=[[id]],additionalProperties={}], io.airbyte.protocol.models.ConfiguredAirbyteStream@6aa478e0[stream=io.airbyte.protocol.models.AirbyteStream@678184a8[name=tags,jsonSchema={"type":["null","object"],"properties":{"name":{"type":["null","string"]},"count":{"type":["null","integer"]}}},supportedSyncModes=[full_refresh],sourceDefinedCursor=<null>,defaultCursorField=[],sourceDefinedPrimaryKey=[[name]],namespace=<null>,additionalProperties={}],syncMode=full_refresh,cursorField=[],destinationSyncMode=append,primaryKey=[[name]],additionalProperties={}], io.airbyte.protocol.models.ConfiguredAirbyteStream@a2b84ef[stream=io.airbyte.protocol.models.AirbyteStream@48371468[name=ticket_fields,jsonSchema={"type":["null","object"],"properties":{"id":{"type":["null","integer"]},"tag":{"type":["null","string"]},"url":{"type":["null","string"]},"type":{"type":["null","string"]},"title":{"type":["null","string"]},"active":{"type":["null","boolean"]},"position":{"type":["null","integer"]},"required":{"type":["null","boolean"]},"raw_title":{"type":["null","string"]},"removable":{"type":["null","boolean"]},"created_at":{"type":["null","string"],"format":"date-time"},"updated_at":{"type":["null","string"],"format":"date-time"},"description":{"type":["null","string"]},"sub_type_id":{"type":["null","integer"]},"raw_description":{"type":["null","string"]},"title_in_portal":{"type":["null","string"]},"agent_description":{"type":["null","string"]},"visible_in_portal":{"type":["null","boolean"]},"editable_in_portal":{"type":["null","boolean"]},"required_in_portal":{"type":["null","boolean"]},"raw_title_in_portal":{"type":["null","string"]},"collapsed_for_agents":{"type":["null","boolean"]},"custom_field_options":{"type":["null","array"],"items":{"type":["null","object"],"properties":{"id":{"type":["null","integer"]},"name":{"type":["null","string"]},"value":{"type":["null","string"]},"default":{"type":["null","boolean"]},"raw_name":{"type":["null","string"]}}}},"system_field_options":{"type":["null","array"]},"regexp_for_validation":{"type":["null","string"]}}},supportedSyncModes=[full_refresh, incremental],sourceDefinedCursor=true,defaultCursorField=[updated_at],sourceDefinedPrimaryKey=[[id]],namespace=<null>,additionalProperties={}],syncMode=incremental,cursorField=[updated_at],destinationSyncMode=append_dedup,primaryKey=[[id]],additionalProperties={}], io.airbyte.protocol.models.ConfiguredAirbyteStream@11d16f6[stream=io.airbyte.protocol.models.AirbyteStream@6a5266b9[name=ticket_metric_events,jsonSchema={"type":["null","object"],"properties":{"id":{"type":["null","integer"]},"time":{"type":["null","string"]},"type":{"type":["null","string"]},"metric":{"type":["null","string"]},"ticket_id":{"type":["null","integer"]},"instance_id":{"type":["null","integer"]}}},supportedSyncModes=[full_refresh, incremental],sourceDefinedCursor=true,defaultCursorField=[time],sourceDefinedPrimaryKey=[[id]],namespace=<null>,additionalProperties={}],syncMode=incremental,cursorField=[time],destinationSyncMode=append_dedup,primaryKey=[[id]],additionalProperties={}], io.airbyte.protocol.models.ConfiguredAirbyteStream@3c089be0[stream=io.airbyte.protocol.models.AirbyteStream@4cec5c5d[name=ticket_metrics,jsonSchema={"type":["null","object"],"properties":{"id":{"type":["null","integer"]},"url":{"type":["null","string"]},"time":{"type":["null","string"]},"type":{"type":["null","string"]},"metric":{"type":["null","string"]},"status":{"type":["null","object"],"properties":{"business":{"type":["null","integer"]},"calendar":{"type":["null","integer"]}}},"reopens":{"type":["null","integer"]},"replies":{"type":["null","integer"]},"solved_at":{"type":["null","string"],"format":"date-time"},"ticket_id":{"type":["null","integer"]},"created_at":{"type":["null","string"],"format":"date-time"},"updated_at":{"type":["null","string"],"format":"date-time"},"assigned_at":{"type":["null","string"],"format":"date-time"},"instance_id":{"type":["null","integer"]},"group_stations":{"type":["null","integer"]},"assignee_stations":{"type":["null","integer"]},"status_updated_at":{"type":["null","string"],"format":"date-time"},"assignee_updated_at":{"type":["null","string"],"format":"date-time"},"requester_updated_at":{"type":["null","string"],"format":"date-time"},"initially_assigned_at":{"type":["null","string"],"format":"date-time"},"reply_time_in_minutes":{"type":["null","object"],"properties":{"business":{"type":["null","integer"]},"calendar":{"type":["null","integer"]}}},"latest_comment_added_at":{"type":["null","string"],"format":"date-time"},"on_hold_time_in_minutes":{"type":["null","object"],"properties":{"business":{"type":["null","integer"]},"calendar":{"type":["null","integer"]}}},"agent_wait_time_in_minutes":{"type":["null","object"],"properties":{"business":{"type":["null","integer"]},"calendar":{"type":["null","integer"]}}},"requester_wait_time_in_minutes":{"type":["null","object"],"properties":{"business":{"type":["null","integer"]},"calendar":{"type":["null","integer"]}}},"full_resolution_time_in_minutes":{"type":["null","object"],"properties":{"business":{"type":["null","integer"]},"calendar":{"type":["null","integer"]}}},"first_resolution_time_in_minutes":{"type":["null","object"],"properties":{"business":{"type":["null","integer"]},"calendar":{"type":["null","integer"]}}}}},supportedSyncModes=[full_refresh, incremental],sourceDefinedCursor=true,defaultCursorField=[updated_at],sourceDefinedPrimaryKey=[[id]],namespace=<null>,additionalProperties={}],syncMode=incremental,cursorField=[updated_at],destinationSyncMode=append_dedup,primaryKey=[[id]],additionalProperties={}], io.airbyte.protocol.models.ConfiguredAirbyteStream@252f5990[stream=io.airbyte.protocol.models.AirbyteStream@5cb19422[name=tickets,jsonSchema={"type":["null","object"],"properties":{"id":{"type":["null","integer"]},"url":{"type":["null","string"]},"via":{"type":["null","object"],"properties":{"source":{"type":["null","object"],"properties":{"to":{"type":["null","object"],"properties":{"name":{"type":["null","string"]},"phone":{"type":["null","string"]},"address":{"type":["null","string"]},"username":{"type":["null","string"]},"email_ccs":{"type":["null","string"]},"facebook_id":{"type":["null","string"]},"profile_url":{"type":["null","string"]},"formatted_phone":{"type":["null","string"]}}},"rel":{"type":["null","string"]},"from":{"type":["null","object"],"properties":{"id":{"type":["null","integer"]},"name":{"type":["null","string"]},"phone":{"type":["null","string"]},"title":{"type":["null","string"]},"address":{"type":["null","string"]},"deleted":{"type":["null","boolean"]},"subject":{"type":["null","string"]},"topic_id":{"type":["null","integer"]},"username":{"type":["null","string"]},"ticket_id":{"type":["null","integer"]},"topic_name":{"type":["null","string"]},"facebook_id":{"type":["null","string"]},"profile_url":{"type":["null","string"]},"revision_id":{"type":["null","integer"]},"formatted_phone":{"type":["null","string"]},"original_recipients":{"type":["null","array"],"items":{"type":["null","string"]}}}}}},"channel":{"type":["null","string"]}}},"tags":{"type":["null","array"],"items":{"type":["null","string"]}},"type":{"type":["null","string"]},"due_at":{"type":["null","string"],"format":"date-time"},"status":{"type":["null","string"]},"subject":{"type":["null","string"]},"brand_id":{"type":["null","integer"]},"group_id":{"type":["null","integer"]},"priority":{"type":["null","string"]},"is_public":{"type":["null","boolean"]},"recipient":{"type":["null","string"]},"created_at":{"type":["null","string"],"format":"date-time"},"problem_id":{"type":["null","integer"]},"updated_at":{"type":["null","string"],"format":"date-time"},"assignee_id":{"type":["null","integer"]},"description":{"type":["null","string"]},"external_id":{"type":["null","string"]},"raw_subject":{"type":["null","string"]},"email_cc_ids":{"type":["null","array"],"items":{"type":["null","integer"]}},"follower_ids":{"type":["null","array"],"items":{"type":["null","integer"]}},"followup_ids":{"type":["null","array"],"items":{"type":["null","integer"]}},"requester_id":{"type":["null","integer"]},"submitter_id":{"type":["null","integer"]},"custom_fields":{"type":["null","array"],"items":{"type":["null","object"],"properties":{"id":{"type":["null","integer"]},"value":{"type":["null","string"]}}}},"has_incidents":{"type":["null","boolean"]},"forum_topic_id":{"type":["null","integer"]},"ticket_form_id":{"type":["null","integer"]},"organization_id":{"type":["null","integer"]},"collaborator_ids":{"type":["null","array"],"items":{"type":["null","integer"]}},"allow_attachments":{"type":["null","boolean"]},"allow_channelback":{"type":["null","boolean"]},"generated_timestamp":{"type":["null","integer"]},"satisfaction_rating":{"type":["null","object","string"],"properties":{"id":{"type":["null","integer"]},"url":{"type":["null","string"]},"score":{"type":["null","string"]},"reason":{"type":["null","string"]},"comment":{"type":["null","string"]},"group_id":{"type":["null","integer"]},"reason_id":{"type":["null","integer"]},"ticket_id":{"type":["null","integer"]},"created_at":{"type":["null","string"],"format":"date-time"},"updated_at":{"type":["null","string"],"format":"date-time"},"assignee_id":{"type":["null","integer"]},"requester_id":{"type":["null","integer"]}}},"sharing_agreement_ids":{"type":["null","array"],"items":{"type":["null","integer"]}}}},supportedSyncModes=[full_refresh, incremental],sourceDefinedCursor=true,defaultCursorField=[updated_at],sourceDefinedPrimaryKey=[[id]],namespace=<null>,additionalProperties={}],syncMode=incremental,cursorField=[updated_at],destinationSyncMode=append_dedup,primaryKey=[[id]],additionalProperties={}]],additionalProperties={}],failures=[io.airbyte.config.FailureReason@241ec3cf[failureOrigin=source,failureType=<null>,internalMessage=io.airbyte.workers.DefaultReplicationWorker$SourceException: Source process exited with non-zero exit code 1,externalMessage=Something went wrong within the source connector,metadata=io.airbyte.config.Metadata@a3e6da5[additionalProperties={attemptNumber=0, jobId=37}],stacktrace=java.util.concurrent.CompletionException: io.airbyte.workers.DefaultReplicationWorker$SourceException: Source process exited with non-zero exit code 1
	at java.base/java.util.concurrent.CompletableFuture.encodeThrowable(CompletableFuture.java:315)
	at java.base/java.util.concurrent.CompletableFuture.completeThrowable(CompletableFuture.java:320)
	at java.base/java.util.concurrent.CompletableFuture$AsyncRun.run(CompletableFuture.java:1807)
	at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
	at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
	at java.base/java.lang.Thread.run(Thread.java:833)
Caused by: io.airbyte.workers.DefaultReplicationWorker$SourceException: Source process exited with non-zero exit code 1
	at io.airbyte.workers.DefaultReplicationWorker.lambda$getReplicationRunnable$5(DefaultReplicationWorker.java:310)
	at java.base/java.util.concurrent.CompletableFuture$AsyncRun.run(CompletableFuture.java:1804)
	... 3 more
,retryable=<null>,timestamp=1650373997433]]]
2022-04-19 13:13:47 INFO i.a.w.t.TemporalUtils(withBackgroundHeartbeat):234 - Stopping temporal heartbeating...
2022-04-19 13:13:48 INFO i.a.c.p.ConfigRepository(updateConnectionState):545 - Updating connection b50f36e6-70ca-4384-bd12-93d1f650e999 state: io.airbyte.config.State@57906371[state={"ticket_fields":{"updated_at":"2022-04-14T06:20:39Z"},"ticket_metric_events":{"time":"2022-04-19T10:29:34Z"}}]
2022-04-19 13:13:48 INFO i.a.w.t.TemporalAttemptExecution(get):105 - Docker volume job log path: /tmp/workspace/37/0/logs.log
2022-04-19 13:13:48 INFO i.a.w.t.TemporalAttemptExecution(get):110 - Executing worker wrapper. Airbyte version: 0.35.30-alpha
2022-04-19 13:13:48 INFO i.a.w.DefaultNormalizationWorker(run):46 - Running normalization.
2022-04-19 13:13:48 INFO i.a.w.n.DefaultNormalizationRunner(runProcess):122 - Running with normalization version: airbyte/normalization:0.1.66
2022-04-19 13:13:48 INFO i.a.c.i.LineGobbler(voidCall):82 - Checking if airbyte/normalization:0.1.66 exists...
2022-04-19 13:13:49 INFO i.a.c.i.LineGobbler(voidCall):82 - airbyte/normalization:0.1.66 was found locally.
2022-04-19 13:13:49 INFO i.a.w.p.DockerProcessFactory(create):157 - Preparing command: docker run --rm --init -i -w /data/37/0/normalize --log-driver none --network host -v airbyte_workspace:/data -v /tmp/airbyte_local:/local airbyte/normalization:0.1.66 run --integration-type redshift --config destination_config.json --catalog destination_catalog.json
2022-04-19 13:13:52 normalization > Running: transform-config --config destination_config.json --integration-type redshift --out /data/37/0/normalize
2022-04-19 13:13:55 normalization > Namespace(config='destination_config.json', integration_type=<DestinationType.redshift: 'redshift'>, out='/data/37/0/normalize')
2022-04-19 13:13:55 normalization > transform_redshift
2022-04-19 13:13:55 normalization > Running: transform-catalog --integration-type redshift --profile-config-dir /data/37/0/normalize --catalog destination_catalog.json --out /data/37/0/normalize/models/generated/ --json-column _airbyte_data
2022-04-19 13:14:03 normalization > Processing destination_catalog.json...
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/brands_ab1.sql from brands
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/brands_ab2.sql from brands
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/brands_ab3.sql from brands
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/zendesk/brands.sql from brands
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/sla_policies_ab1.sql from sla_policies
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/sla_policies_ab2.sql from sla_policies
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/sla_policies_ab3.sql from sla_policies
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/zendesk/sla_policies.sql from sla_policies
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/tags_ab1.sql from tags
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/tags_ab2.sql from tags
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/tags_ab3.sql from tags
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/zendesk/tags.sql from tags
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_fields_ab1.sql from ticket_fields
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_fields_ab2.sql from ticket_fields
2022-04-19 13:14:03 normalization >   Generating airbyte_views/zendesk/ticket_fields_stg.sql from ticket_fields
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/scd/zendesk/ticket_fields_scd.sql from ticket_fields
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/zendesk/ticket_fields.sql from ticket_fields
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metric_events_ab1.sql from ticket_metric_events
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metric_events_ab2.sql from ticket_metric_events
2022-04-19 13:14:03 normalization >   Generating airbyte_views/zendesk/ticket_metric_events_stg.sql from ticket_metric_events
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/scd/zendesk/ticket_metric_events_scd.sql from ticket_metric_events
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/zendesk/ticket_metric_events.sql from ticket_metric_events
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_ab1.sql from ticket_metrics
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_ab2.sql from ticket_metrics
2022-04-19 13:14:03 normalization >   Generating airbyte_views/zendesk/ticket_metrics_stg.sql from ticket_metrics
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/scd/zendesk/ticket_metrics_scd.sql from ticket_metrics
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/zendesk/ticket_metrics.sql from ticket_metrics
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/tickets_ab1.sql from tickets
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/tickets_ab2.sql from tickets
2022-04-19 13:14:03 normalization >   Generating airbyte_views/zendesk/tickets_stg.sql from tickets
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/scd/zendesk/tickets_scd.sql from tickets
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/zendesk/tickets.sql from tickets
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/sla_policies_filter_ab1.sql from sla_policies/filter
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/sla_policies_filter_ab2.sql from sla_policies/filter
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/sla_policies_filter_ab3.sql from sla_policies/filter
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/zendesk/sla_policies_filter.sql from sla_policies/filter
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/sla_policies_policy_metrics_ab1.sql from sla_policies/policy_metrics
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/sla_policies_policy_metrics_ab2.sql from sla_policies/policy_metrics
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/sla_policies_policy_metrics_ab3.sql from sla_policies/policy_metrics
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/zendesk/sla_policies_policy_metrics.sql from sla_policies/policy_metrics
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_fields_custom_field_options_ab1.sql from ticket_fields/custom_field_options
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_fields_custom_field_options_ab2.sql from ticket_fields/custom_field_options
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_fields_custom_field_options_ab3.sql from ticket_fields/custom_field_options
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/zendesk/ticket_fields_custom_field_options.sql from ticket_fields/custom_field_options
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_status_ab1.sql from ticket_metrics/status
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_status_ab2.sql from ticket_metrics/status
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_status_ab3.sql from ticket_metrics/status
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/zendesk/ticket_metrics_status.sql from ticket_metrics/status
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_reply_time_in_minutes_ab1.sql from ticket_metrics/reply_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_reply_time_in_minutes_ab2.sql from ticket_metrics/reply_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_reply_time_in_minutes_ab3.sql from ticket_metrics/reply_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/zendesk/ticket_metrics_reply_time_in_minutes.sql from ticket_metrics/reply_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_on_hold_time_in_minutes_ab1.sql from ticket_metrics/on_hold_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_on_hold_time_in_minutes_ab2.sql from ticket_metrics/on_hold_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_on_hold_time_in_minutes_ab3.sql from ticket_metrics/on_hold_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/zendesk/ticket_metrics_on_hold_time_in_minutes.sql from ticket_metrics/on_hold_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_agent_wait_time_in_minutes_ab1.sql from ticket_metrics/agent_wait_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_agent_wait_time_in_minutes_ab2.sql from ticket_metrics/agent_wait_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_agent_wait_time_in_minutes_ab3.sql from ticket_metrics/agent_wait_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/zendesk/ticket_metrics_agent_wait_time_in_minutes.sql from ticket_metrics/agent_wait_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_requester_wait_time_in_minutes_ab1.sql from ticket_metrics/requester_wait_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_requester_wait_time_in_minutes_ab2.sql from ticket_metrics/requester_wait_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_requester_wait_time_in_minutes_ab3.sql from ticket_metrics/requester_wait_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/zendesk/ticket_metrics_requester_wait_time_in_minutes.sql from ticket_metrics/requester_wait_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_full_resolution_time_in_minutes_ab1.sql from ticket_metrics/full_resolution_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_full_resolution_time_in_minutes_ab2.sql from ticket_metrics/full_resolution_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_full_resolution_time_in_minutes_ab3.sql from ticket_metrics/full_resolution_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/zendesk/ticket_metrics_full_resolution_time_in_minutes.sql from ticket_metrics/full_resolution_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_first_resolution_time_in_minutes_ab1.sql from ticket_metrics/first_resolution_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_first_resolution_time_in_minutes_ab2.sql from ticket_metrics/first_resolution_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/ticket_metrics_first_resolution_time_in_minutes_ab3.sql from ticket_metrics/first_resolution_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/zendesk/ticket_metrics_first_resolution_time_in_minutes.sql from ticket_metrics/first_resolution_time_in_minutes
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/tickets_via_ab1.sql from tickets/via
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/tickets_via_ab2.sql from tickets/via
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/tickets_via_ab3.sql from tickets/via
2022-04-19 13:14:03 normalization >   Generating airbyte_incremental/zendesk/tickets_via.sql from tickets/via
2022-04-19 13:14:03 normalization >   Ignoring stream 'tags' from tickets/tags because properties list is empty
2022-04-19 13:14:03 normalization >   Ignoring stream 'email_cc_ids' from tickets/email_cc_ids because properties list is empty
2022-04-19 13:14:03 normalization >   Ignoring stream 'follower_ids' from tickets/follower_ids because properties list is empty
2022-04-19 13:14:03 normalization >   Ignoring stream 'followup_ids' from tickets/followup_ids because properties list is empty
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/tickets_custom_fields_ab1.sql from tickets/custom_fields
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/tickets_custom_fields_ab2.sql from tickets/custom_fields
2022-04-19 13:14:03 normalization >   Generating airbyte_ctes/zendesk/tickets_custom_fields_ab3.sql from tickets/custom_fields
2022-04-19 13:14:05 normalization >   Generating airbyte_incremental/zendesk/tickets_custom_fields.sql from tickets/custom_fields
2022-04-19 13:14:05 normalization >   Ignoring stream 'collaborator_ids' from tickets/collaborator_ids because properties list is empty
2022-04-19 13:14:05 normalization >   Generating airbyte_ctes/zendesk/tickets_satisfaction_rating_ab1.sql from tickets/satisfaction_rating
2022-04-19 13:14:05 normalization >   Generating airbyte_ctes/zendesk/tickets_satisfaction_rating_ab2.sql from tickets/satisfaction_rating
2022-04-19 13:14:05 normalization >   Generating airbyte_ctes/zendesk/tickets_satisfaction_rating_ab3.sql from tickets/satisfaction_rating
2022-04-19 13:14:05 normalization >   Generating airbyte_incremental/zendesk/tickets_satisfaction_rating.sql from tickets/satisfaction_rating
2022-04-19 13:14:05 normalization >   Ignoring stream 'sharing_agreement_ids' from tickets/sharing_agreement_ids because properties list is empty
2022-04-19 13:14:05 normalization >   Generating airbyte_ctes/zendesk/sla_policies_filter_all_ab1.sql from sla_policies/filter/all
2022-04-19 13:14:05 normalization >   Generating airbyte_ctes/zendesk/sla_policies_filter_all_ab2.sql from sla_policies/filter/all
2022-04-19 13:14:05 normalization >   Generating airbyte_ctes/zendesk/sla_policies_filter_all_ab3.sql from sla_policies/filter/all
2022-04-19 13:14:05 normalization >   Generating airbyte_incremental/zendesk/sla_policies_filter_all.sql from sla_policies/filter/all
2022-04-19 13:14:05 normalization >   Generating airbyte_ctes/zendesk/sla_policies_filter_any_ab1.sql from sla_policies/filter/any
2022-04-19 13:14:05 normalization >   Generating airbyte_ctes/zendesk/sla_policies_filter_any_ab2.sql from sla_policies/filter/any
2022-04-19 13:14:05 normalization >   Generating airbyte_ctes/zendesk/sla_policies_filter_any_ab3.sql from sla_policies/filter/any
2022-04-19 13:14:05 normalization >   Generating airbyte_incremental/zendesk/sla_policies_filter_any.sql from sla_policies/filter/any
2022-04-19 13:14:05 normalization >   Generating airbyte_ctes/zendesk/tickets_via_source_ab1.sql from tickets/via/source
2022-04-19 13:14:05 normalization >   Generating airbyte_ctes/zendesk/tickets_via_source_ab2.sql from tickets/via/source
2022-04-19 13:14:05 normalization >   Generating airbyte_ctes/zendesk/tickets_via_source_ab3.sql from tickets/via/source
2022-04-19 13:14:05 normalization >   Generating airbyte_incremental/zendesk/tickets_via_source.sql from tickets/via/source
2022-04-19 13:14:05 normalization >   Generating airbyte_ctes/zendesk/tickets_via_source_to_ab1.sql from tickets/via/source/to
2022-04-19 13:14:05 normalization >   Generating airbyte_ctes/zendesk/tickets_via_source_to_ab2.sql from tickets/via/source/to
2022-04-19 13:14:05 normalization >   Generating airbyte_ctes/zendesk/tickets_via_source_to_ab3.sql from tickets/via/source/to
2022-04-19 13:14:05 normalization >   Generating airbyte_incremental/zendesk/tickets_via_source_to.sql from tickets/via/source/to
2022-04-19 13:14:05 normalization >   Generating airbyte_ctes/zendesk/tickets_via_source_from_ab1.sql from tickets/via/source/from
2022-04-19 13:14:05 normalization >   Generating airbyte_ctes/zendesk/tickets_via_source_from_ab2.sql from tickets/via/source/from
2022-04-19 13:14:05 normalization >   Generating airbyte_ctes/zendesk/tickets_via_source_from_ab3.sql from tickets/via/source/from
2022-04-19 13:14:05 normalization >   Generating airbyte_incremental/zendesk/tickets_via_source_from.sql from tickets/via/source/from
2022-04-19 13:14:05 normalization >   Ignoring stream 'original_recipients' from tickets/via/source/from/original_recipients because properties list is empty
2022-04-19 13:14:05 normalization > detected no config file for ssh, assuming ssh is off.
2022-04-19 13:14:29 normalization > Running with dbt=0.21.1
2022-04-19 13:14:34 normalization > Unable to do partial parsing because ../build/partial_parse.msgpack not found
2022-04-19 13:15:05 normalization > [WARNING]: Configuration paths exist in your dbt_project.yml file which do not apply to any resources.
2022-04-19 13:15:05 normalization > There are 1 unused configuration paths:
2022-04-19 13:15:05 normalization > - models.airbyte_utils.generated.airbyte_tables
2022-04-19 13:15:05 normalization > 
2022-04-19 13:15:05 normalization > Found 104 models, 0 tests, 0 snapshots, 0 analyses, 520 macros, 0 operations, 0 seed files, 7 sources, 0 exposures
2022-04-19 13:15:06 normalization > 
2022-04-19 13:15:06 normalization > 13:15:06 | Concurrency: 4 threads (target='prod')
2022-04-19 13:15:06 normalization > 13:15:06 | 
2022-04-19 13:15:07 normalization > 13:15:07 | 1 of 33 START view model _airbyte_zendesk.ticket_metric_events_stg........................................... [RUN]
2022-04-19 13:15:07 normalization > 13:15:07 | 2 of 33 START incremental model zendesk.tags................................................................. [RUN]
2022-04-19 13:15:08 normalization > 13:15:08 | 4 of 33 START incremental model zendesk.sla_policies......................................................... [RUN]
2022-04-19 13:15:08 normalization > 13:15:08 | 3 of 33 START view model _airbyte_zendesk.ticket_fields_stg.................................................. [RUN]
2022-04-19 13:15:08 normalization > 13:15:08 | 1 of 33 OK created view model _airbyte_zendesk.ticket_metric_events_stg...................................... [CREATE VIEW in 1.07s]
2022-04-19 13:15:08 normalization > 13:15:08 | 5 of 33 START view model _airbyte_zendesk.ticket_metrics_stg................................................. [RUN]
2022-04-19 13:15:09 normalization > 13:15:09 | 3 of 33 OK created view model _airbyte_zendesk.ticket_fields_stg............................................. [CREATE VIEW in 1.26s]
2022-04-19 13:15:09 normalization > 13:15:09 | 6 of 33 START incremental model zendesk.brands............................................................... [RUN]
2022-04-19 13:15:09 normalization > 13:15:09 | 5 of 33 OK created view model _airbyte_zendesk.ticket_metrics_stg............................................ [CREATE VIEW in 1.14s]
2022-04-19 13:15:09 normalization > 13:15:09 | 7 of 33 START view model _airbyte_zendesk.tickets_stg........................................................ [RUN]
2022-04-19 13:15:10 normalization > 13:15:10 | 7 of 33 OK created view model _airbyte_zendesk.tickets_stg................................................... [CREATE VIEW in 0.61s]
2022-04-19 13:15:10 normalization > 13:15:10 | 8 of 33 START incremental model zendesk.ticket_metric_events_scd............................................. [RUN]
2022-04-19 13:15:12 normalization > 13:15:12 | 4 of 33 OK created incremental model zendesk.sla_policies.................................................... [INSERT 0 11 in 4.71s]
2022-04-19 13:15:12 normalization > 13:15:12 | 2 of 33 OK created incremental model zendesk.tags............................................................ [INSERT 0 196 in 5.10s]
2022-04-19 13:15:12 normalization > 13:15:12 | 9 of 33 START incremental model zendesk.ticket_fields_scd.................................................... [RUN]
2022-04-19 13:15:12 normalization > 13:15:12 | 10 of 33 START incremental model zendesk.ticket_metrics_scd.................................................. [RUN]
2022-04-19 13:15:13 normalization > 13:15:13 | 6 of 33 OK created incremental model zendesk.brands.......................................................... [INSERT 0 12 in 4.19s]
2022-04-19 13:15:13 normalization > 13:15:13 | 11 of 33 START incremental model zendesk.tickets_scd......................................................... [RUN]
2022-04-19 13:15:17 normalization > 13:15:17 | 8 of 33 OK created incremental model zendesk.ticket_metric_events_scd........................................ [INSERT 0 11088 in 7.48s]
2022-04-19 13:15:17 normalization > 13:15:17 | 12 of 33 START incremental model zendesk.ticket_metric_events................................................ [RUN]
2022-04-19 13:15:20 normalization > 13:15:20 | 11 of 33 ERROR creating incremental model zendesk.tickets_scd................................................ [ERROR in 6.47s]
2022-04-19 13:15:20 normalization > 13:15:20 | 13 of 33 SKIP relation zendesk.tickets....................................................................... [SKIP]
2022-04-19 13:15:20 normalization > 13:15:20 | 14 of 33 START incremental model zendesk.sla_policies_filter................................................. [RUN]
2022-04-19 13:15:20 normalization > 13:15:20 | 9 of 33 OK created incremental model zendesk.ticket_fields_scd............................................... [INSERT 0 28 in 7.84s]
2022-04-19 13:15:20 normalization > 13:15:20 | 15 of 33 START incremental model zendesk.sla_policies_policy_metrics......................................... [RUN]
2022-04-19 13:15:22 normalization > 13:15:22 | 12 of 33 OK created incremental model zendesk.ticket_metric_events........................................... [INSERT 0 163043 in 5.04s]
2022-04-19 13:15:22 normalization > 13:15:22 | 16 of 33 SKIP relation zendesk.tickets_custom_fields......................................................... [SKIP]
2022-04-19 13:15:22 normalization > 13:15:22 | 17 of 33 START incremental model zendesk.ticket_fields....................................................... [RUN]
2022-04-19 13:15:22 normalization > 13:15:22 | 14 of 33 OK created incremental model zendesk.sla_policies_filter............................................ [INSERT 0 11 in 2.82s]
2022-04-19 13:15:23 normalization > 13:15:23 | 18 of 33 SKIP relation zendesk.tickets_satisfaction_rating................................................... [SKIP]
2022-04-19 13:15:23 normalization > 13:15:23 | 19 of 33 SKIP relation zendesk.tickets_via................................................................... [SKIP]
2022-04-19 13:15:23 normalization > 13:15:23 | 20 of 33 START incremental model zendesk.ticket_fields_custom_field_options.................................. [RUN]
2022-04-19 13:15:23 normalization > 13:15:23 | 15 of 33 OK created incremental model zendesk.sla_policies_policy_metrics.................................... [INSERT 0 264 in 3.31s]
2022-04-19 13:15:23 normalization > 13:15:23 | 21 of 33 START incremental model zendesk.sla_policies_filter_all............................................. [RUN]
2022-04-19 13:15:27 normalization > 13:15:27 | 21 of 33 OK created incremental model zendesk.sla_policies_filter_all........................................ [INSERT 0 11 in 3.91s]
2022-04-19 13:15:27 normalization > 13:15:27 | 22 of 33 START incremental model zendesk.sla_policies_filter_any............................................. [RUN]
2022-04-19 13:15:28 normalization > 13:15:28 | 20 of 33 OK created incremental model zendesk.ticket_fields_custom_field_options............................. [INSERT 0 202 in 4.43s]
2022-04-19 13:15:28 normalization > 13:15:28 | 23 of 33 SKIP relation zendesk.tickets_via_source............................................................ [SKIP]
2022-04-19 13:15:28 normalization > 13:15:28 | 24 of 33 SKIP relation zendesk.tickets_via_source_from....................................................... [SKIP]
2022-04-19 13:15:28 normalization > 13:15:28 | 25 of 33 SKIP relation zendesk.tickets_via_source_to......................................................... [SKIP]
2022-04-19 13:15:29 normalization > 13:15:29 | 10 of 33 OK created incremental model zendesk.ticket_metrics_scd............................................. [INSERT 0 29506 in 17.19s]
2022-04-19 13:15:29 normalization > 13:15:29 | 26 of 33 START incremental model zendesk.ticket_metrics...................................................... [RUN]
2022-04-19 13:15:30 normalization > 13:15:30 | 27 of 33 START incremental model zendesk.ticket_metrics_agent_wait_time_in_minutes........................... [RUN]
2022-04-19 13:15:31 normalization > 13:15:31 | 22 of 33 OK created incremental model zendesk.sla_policies_filter_any........................................ [INSERT 0 11 in 3.85s]
2022-04-19 13:15:31 normalization > 13:15:31 | 28 of 33 START incremental model zendesk.ticket_metrics_first_resolution_time_in_minutes..................... [RUN]
2022-04-19 13:15:36 normalization > 13:15:36 | 26 of 33 OK created incremental model zendesk.ticket_metrics................................................. [INSERT 0 17020 in 6.19s]
2022-04-19 13:15:36 normalization > 13:15:36 | 27 of 33 OK created incremental model zendesk.ticket_metrics_agent_wait_time_in_minutes...................... [INSERT 0 67745 in 5.78s]
2022-04-19 13:15:36 normalization > 13:15:36 | 29 of 33 START incremental model zendesk.ticket_metrics_full_resolution_time_in_minutes...................... [RUN]
2022-04-19 13:15:36 normalization > 13:15:36 | 30 of 33 START incremental model zendesk.ticket_metrics_on_hold_time_in_minutes.............................. [RUN]
2022-04-19 13:15:38 normalization > 13:15:38 | 28 of 33 OK created incremental model zendesk.ticket_metrics_first_resolution_time_in_minutes................ [INSERT 0 67745 in 6.54s]
2022-04-19 13:15:38 normalization > 13:15:38 | 17 of 33 OK created incremental model zendesk.ticket_fields.................................................. [INSERT 0 14 in 15.42s]
2022-04-19 13:15:38 normalization > 13:15:38 | 31 of 33 START incremental model zendesk.ticket_metrics_reply_time_in_minutes................................ [RUN]
2022-04-19 13:15:38 normalization > 13:15:38 | 32 of 33 START incremental model zendesk.ticket_metrics_requester_wait_time_in_minutes....................... [RUN]
2022-04-19 13:15:41 normalization > 13:15:41 | 29 of 33 OK created incremental model zendesk.ticket_metrics_full_resolution_time_in_minutes................. [INSERT 0 67745 in 5.22s]
2022-04-19 13:15:41 normalization > 13:15:41 | 33 of 33 START incremental model zendesk.ticket_metrics_status............................................... [RUN]
2022-04-19 13:15:41 normalization > 13:15:41 | 30 of 33 OK created incremental model zendesk.ticket_metrics_on_hold_time_in_minutes......................... [INSERT 0 67745 in 5.41s]
2022-04-19 13:15:42 normalization > 13:15:42 | 32 of 33 OK created incremental model zendesk.ticket_metrics_requester_wait_time_in_minutes.................. [INSERT 0 67745 in 4.37s]
2022-04-19 13:15:43 normalization > 13:15:43 | 31 of 33 OK created incremental model zendesk.ticket_metrics_reply_time_in_minutes........................... [INSERT 0 67745 in 4.83s]
2022-04-19 13:15:43 normalization > 13:15:43 | 33 of 33 OK created incremental model zendesk.ticket_metrics_status.......................................... [INSERT 0 0 in 2.29s]
2022-04-19 13:15:43 normalization > 13:15:43 | 
2022-04-19 13:15:43 normalization > 13:15:43 | Finished running 4 view models, 29 incremental models in 37.71s.
2022-04-19 13:15:43 normalization > 
2022-04-19 13:15:43 normalization > Completed with 1 error and 0 warnings:
2022-04-19 13:15:43 normalization > 
2022-04-19 13:15:43 normalization > Database Error in model tickets_scd (models/generated/airbyte_incremental/scd/zendesk/tickets_scd.sql)
2022-04-19 13:15:43 normalization >   column "_airbyte_start_at__dbt_alter" is of type timestamp with time zone but expression is of type bigint
2022-04-19 13:15:43 normalization >   HINT:  You will need to rewrite or cast the expression.
2022-04-19 13:15:43 normalization > 
2022-04-19 13:15:43 normalization > Done. PASS=25 WARN=0 ERROR=1 SKIP=7 TOTAL=33
2022-04-19 13:15:43 normalization > 
2022-04-19 13:15:43 normalization > Diagnosing dbt debug to check if destination is available for dbt and well configured (1):
2022-04-19 13:15:43 normalization > 
2022-04-19 13:15:46 normalization > Running with dbt=0.21.1
2022-04-19 13:15:46 normalization > dbt version: 0.21.1
2022-04-19 13:15:46 normalization > python version: 3.8.12
2022-04-19 13:15:46 normalization > python path: /usr/local/bin/python
2022-04-19 13:15:46 normalization > os info: Linux-5.10.75-79.358.amzn2.x86_64-x86_64-with-glibc2.2.5
2022-04-19 13:15:46 normalization > Using profiles.yml file at /data/37/0/normalize/profiles.yml
2022-04-19 13:15:46 normalization > Using dbt_project.yml file at /data/37/0/normalize/dbt_project.yml
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > Configuration:
2022-04-19 13:15:46 normalization >   profiles.yml file [OK found and valid]
2022-04-19 13:15:46 normalization >   dbt_project.yml file [OK found and valid]
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > Required dependencies:
2022-04-19 13:15:46 normalization >  - git [OK found]
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > Connection:
2022-04-19 13:15:46 normalization >   host: travlr-data-lake.cs2yeo76upoy.ap-southeast-2.redshift.amazonaws.com
2022-04-19 13:15:46 normalization >   port: 5439
2022-04-19 13:15:46 normalization >   user: travlrdatamaster
2022-04-19 13:15:46 normalization >   database: datalake
2022-04-19 13:15:46 normalization >   schema: zendesk
2022-04-19 13:15:46 normalization >   search_path: None
2022-04-19 13:15:46 normalization >   keepalives_idle: 240
2022-04-19 13:15:46 normalization >   sslmode: None
2022-04-19 13:15:46 normalization >   method: database
2022-04-19 13:15:46 normalization >   cluster_id: None
2022-04-19 13:15:46 normalization >   iam_profile: None
2022-04-19 13:15:46 normalization >   iam_duration_seconds: 900
2022-04-19 13:15:46 normalization >   Connection test: [OK connection ok]
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > All checks passed!
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > Forward dbt output logs to diagnose/debug errors (0):
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:29.881321 (MainThread): Running with dbt=0.21.1
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:34.361962 (MainThread): running dbt with arguments Namespace(cls=<class 'dbt.task.run.RunTask'>, debug=False, defer=None, exclude=None, fail_fast=False, full_refresh=False, log_cache_events=False, log_format='default', partial_parse=None, profile=None, profiles_dir='/data/37/0/normalize', project_dir='/data/37/0/normalize', record_timing_info=None, rpc_method='run', select=None, selector_name=None, single_threaded=False, state=None, strict=False, target=None, test_new_parser=False, threads=None, use_cache=True, use_colors=None, use_experimental_parser=False, vars='{}', version_check=True, warn_error=False, which='run', write_json=True)
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:34.362635 (MainThread): Tracking: do not track
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:34.755234 (MainThread): Unable to do partial parsing because ../build/partial_parse.msgpack not found
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:35.502124 (MainThread): Parsing macros/get_custom_schema.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:35.503431 (MainThread): Parsing macros/incremental.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:35.754317 (MainThread): Parsing macros/should_full_refresh.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:35.846395 (MainThread): Parsing macros/star_intersect.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:35.940426 (MainThread): Parsing macros/cross_db_utils/array.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:36.133429 (MainThread): Parsing macros/cross_db_utils/concat.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:36.157035 (MainThread): Parsing macros/cross_db_utils/current_timestamp.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:36.158042 (MainThread): Parsing macros/cross_db_utils/datatypes.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:36.257137 (MainThread): Parsing macros/cross_db_utils/except.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:36.289892 (MainThread): Parsing macros/cross_db_utils/hash.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:36.323798 (MainThread): Parsing macros/cross_db_utils/json_operations.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:36.743504 (MainThread): Parsing macros/cross_db_utils/quote.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:36.745712 (MainThread): Parsing macros/cross_db_utils/surrogate_key.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:36.766573 (MainThread): Parsing macros/cross_db_utils/type_conversions.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:36.792765 (MainThread): Parsing macros/schema_tests/equal_rowcount.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:36.819060 (MainThread): Parsing macros/schema_tests/equality.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:36.903273 (MainThread): Parsing macros/adapters.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:37.153497 (MainThread): Parsing macros/catalog.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:37.324468 (MainThread): Parsing macros/relations.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:37.325465 (MainThread): Parsing macros/materializations/snapshot_merge.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:37.326287 (MainThread): Parsing macros/adapters.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:37.631218 (MainThread): Parsing macros/catalog.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:37.689201 (MainThread): Parsing macros/relations.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:37.738456 (MainThread): Parsing macros/materializations/snapshot_merge.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:37.740788 (MainThread): Parsing macros/core.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:37.800913 (MainThread): Parsing macros/adapters/common.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:38.600956 (MainThread): Parsing macros/etc/datetime.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:38.726195 (MainThread): Parsing macros/etc/get_custom_alias.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:38.728337 (MainThread): Parsing macros/etc/get_custom_database.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:38.751011 (MainThread): Parsing macros/etc/get_custom_schema.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:38.795617 (MainThread): Parsing macros/etc/is_incremental.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:38.820500 (MainThread): Parsing macros/etc/query.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:38.821975 (MainThread): Parsing macros/etc/where_subquery.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:38.861486 (MainThread): Parsing macros/materializations/helpers.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:39.132595 (MainThread): Parsing macros/materializations/test.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:39.282257 (MainThread): Parsing macros/materializations/common/merge.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:39.537136 (MainThread): Parsing macros/materializations/incremental/helpers.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:39.576373 (MainThread): Parsing macros/materializations/incremental/incremental.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:39.755842 (MainThread): Parsing macros/materializations/incremental/on_schema_change.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:40.121001 (MainThread): Parsing macros/materializations/seed/seed.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:40.439822 (MainThread): Parsing macros/materializations/snapshot/snapshot.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:40.932851 (MainThread): Parsing macros/materializations/snapshot/snapshot_merge.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:40.972500 (MainThread): Parsing macros/materializations/snapshot/strategies.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:41.364332 (MainThread): Parsing macros/materializations/table/table.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:41.463813 (MainThread): Parsing macros/materializations/view/create_or_replace_view.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:41.517545 (MainThread): Parsing macros/materializations/view/view.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:41.727903 (MainThread): Parsing macros/schema_tests/accepted_values.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:41.792696 (MainThread): Parsing macros/schema_tests/not_null.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:41.817539 (MainThread): Parsing macros/schema_tests/relationships.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:41.833466 (MainThread): Parsing macros/schema_tests/unique.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:41.844474 (MainThread): Parsing macros/cross_db_utils/_is_ephemeral.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:41.856181 (MainThread): Parsing macros/cross_db_utils/_is_relation.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:41.857834 (MainThread): Parsing macros/cross_db_utils/cast_bool_to_text.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:41.884650 (MainThread): Parsing macros/cross_db_utils/concat.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:41.886137 (MainThread): Parsing macros/cross_db_utils/current_timestamp.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:41.903946 (MainThread): Parsing macros/cross_db_utils/datatypes.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.011410 (MainThread): Parsing macros/cross_db_utils/date_trunc.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.024090 (MainThread): Parsing macros/cross_db_utils/dateadd.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.053890 (MainThread): Parsing macros/cross_db_utils/datediff.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.115768 (MainThread): Parsing macros/cross_db_utils/except.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.117483 (MainThread): Parsing macros/cross_db_utils/hash.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.144152 (MainThread): Parsing macros/cross_db_utils/identifier.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.146580 (MainThread): Parsing macros/cross_db_utils/intersect.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.157253 (MainThread): Parsing macros/cross_db_utils/last_day.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.199161 (MainThread): Parsing macros/cross_db_utils/length.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.201080 (MainThread): Parsing macros/cross_db_utils/literal.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.202375 (MainThread): Parsing macros/cross_db_utils/position.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.278098 (MainThread): Parsing macros/cross_db_utils/replace.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.279914 (MainThread): Parsing macros/cross_db_utils/right.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.282936 (MainThread): Parsing macros/cross_db_utils/safe_cast.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.314781 (MainThread): Parsing macros/cross_db_utils/split_part.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.343742 (MainThread): Parsing macros/cross_db_utils/width_bucket.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.483938 (MainThread): Parsing macros/jinja_helpers/log_info.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.485661 (MainThread): Parsing macros/jinja_helpers/pretty_log_format.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.487421 (MainThread): Parsing macros/jinja_helpers/pretty_time.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.603597 (MainThread): Parsing macros/jinja_helpers/slugify.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:42.630215 (MainThread): Parsing macros/materializations/insert_by_period_materialization.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:43.012766 (MainThread): Parsing macros/schema_tests/accepted_range.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:43.081790 (MainThread): Parsing macros/schema_tests/at_least_one.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:43.083867 (MainThread): Parsing macros/schema_tests/cardinality_equality.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:43.135931 (MainThread): Parsing macros/schema_tests/equal_rowcount.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:43.162850 (MainThread): Parsing macros/schema_tests/equality.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:43.215726 (MainThread): Parsing macros/schema_tests/expression_is_true.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:43.325228 (MainThread): Parsing macros/schema_tests/fewer_rows_than.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:43.352082 (MainThread): Parsing macros/schema_tests/mutually_exclusive_ranges.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:43.654911 (MainThread): Parsing macros/schema_tests/not_accepted_values.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:43.764211 (MainThread): Parsing macros/schema_tests/not_constant.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:43.817358 (MainThread): Parsing macros/schema_tests/not_null_proportion.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:44.011205 (MainThread): Parsing macros/schema_tests/recency.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:44.022998 (MainThread): Parsing macros/schema_tests/relationships_where.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:44.120983 (MainThread): Parsing macros/schema_tests/sequential_values.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:44.219362 (MainThread): Parsing macros/schema_tests/test_not_null_where.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:44.348758 (MainThread): Parsing macros/schema_tests/test_unique_where.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:44.351019 (MainThread): Parsing macros/schema_tests/unique_combination_of_columns.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:44.372933 (MainThread): Parsing macros/sql/date_spine.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:44.588783 (MainThread): Parsing macros/sql/generate_series.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:44.719057 (MainThread): Parsing macros/sql/get_column_values.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:44.766616 (MainThread): Parsing macros/sql/get_query_results_as_dict.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:44.832420 (MainThread): Parsing macros/sql/get_relations_by_pattern.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:44.886192 (MainThread): Parsing macros/sql/get_relations_by_prefix.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:44.981465 (MainThread): Parsing macros/sql/get_tables_by_pattern_sql.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:45.151858 (MainThread): Parsing macros/sql/get_tables_by_prefix_sql.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:45.179118 (MainThread): Parsing macros/sql/groupby.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:45.181149 (MainThread): Parsing macros/sql/haversine_distance.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:45.316755 (MainThread): Parsing macros/sql/nullcheck.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:45.319280 (MainThread): Parsing macros/sql/nullcheck_table.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:45.321491 (MainThread): Parsing macros/sql/pivot.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:45.384184 (MainThread): Parsing macros/sql/safe_add.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:45.394973 (MainThread): Parsing macros/sql/star.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:45.464340 (MainThread): Parsing macros/sql/surrogate_key.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:45.503281 (MainThread): Parsing macros/sql/union.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:45.573642 (MainThread): Parsing macros/sql/unpivot.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:45.629970 (MainThread): Parsing macros/web/get_url_host.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:45.640934 (MainThread): Parsing macros/web/get_url_parameter.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:45.643781 (MainThread): Parsing macros/web/get_url_path.sql
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:48.713815 (MainThread): Acquiring new redshift connection "model.airbyte_utils.brands_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:49.108721 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:49.139031 (MainThread): Acquiring new redshift connection "model.airbyte_utils.brands_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:49.339273 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:49.341153 (MainThread): Acquiring new redshift connection "model.airbyte_utils.brands_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:49.507990 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:49.509954 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:49.662656 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:49.766728 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:49.768621 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:49.917875 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:49.940553 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:50.176369 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:50.201482 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tags_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:50.326303 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:50.368452 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tags_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:50.613983 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:50.615829 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tags_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:50.819253 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:50.821620 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:51.351320 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:51.353280 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:51.590691 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:51.593629 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:51.851669 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:51.853559 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:51.924383 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:51.943543 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:52.241108 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:52.391079 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:52.394358 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:52.395076 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:52.395766 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:52.420652 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:52.457993 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:52.461638 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:52.463411 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:52.664279 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:52.666268 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:52.985047 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:53.185852 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:53.188962 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:53.190724 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:53.569263 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:53.598144 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:53.784136 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:53.825038 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:53.949909 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:53.963152 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:54.067340 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:54.069221 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_policy_metrics_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:54.367895 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:54.407281 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_policy_metrics_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:54.442578 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:54.489621 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_policy_metrics_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:54.628725 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:54.651057 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_custom_field_options_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:54.856653 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:54.911193 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_custom_field_options_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:54.964928 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:54.991631 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_custom_field_options_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:55.074912 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:55.113937 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_status_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:55.297712 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:55.503147 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_status_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:55.633648 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:55.635560 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_status_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:55.845446 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:55.884113 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:56.020605 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:56.064468 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:56.159108 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:56.187593 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:56.296221 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:56.298080 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:56.933830 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:56.935902 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:56.997891 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.024413 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.066339 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.084401 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.217149 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.243509 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.277062 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.278908 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.435654 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.452020 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.516501 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.518455 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.569895 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.584496 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.615588 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.617510 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.707622 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.709572 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.810886 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.869919 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.953221 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:57.991972 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:58.102062 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:58.103979 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:58.207120 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:58.240439 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:58.393056 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:58.395062 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:58.452854 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:58.520756 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:58.537385 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:58.678497 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:58.741164 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:58.793263 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:58.842019 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_custom_fields_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:59.029877 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:59.031869 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_custom_fields_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:59.249606 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:59.251482 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_custom_fields_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:59.528939 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:59.531058 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_satisfaction_rating_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:59.880388 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:14:59.923605 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_satisfaction_rating_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.038093 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.073231 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_satisfaction_rating_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.126877 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.138376 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_all_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.235770 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.237742 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_all_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.305334 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.307255 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_all_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.385177 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.387032 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_any_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.482759 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.484690 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_any_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.510081 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.524191 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_any_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.566378 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.568170 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.617481 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.619146 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.634785 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.644966 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.688058 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.743203 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.856548 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:00.858389 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_to_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:01.166334 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:01.169032 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_to_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:01.347912 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:01.374402 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_to_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:01.455980 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:01.458159 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_from_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:01.896703 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:01.898552 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_from_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:02.078937 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:02.206256 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_from_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:02.416208 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:02.427221 (MainThread): Acquiring new redshift connection "model.airbyte_utils.brands".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:02.482541 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:02.484503 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:02.534559 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:02.536613 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tags".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:02.644386 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:02.646294 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_fields".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:02.700857 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:02.703450 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:02.824566 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:02.853504 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:02.960207 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:02.962316 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:03.106479 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:03.108574 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:03.160231 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:03.162241 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:03.260219 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:03.262082 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:03.331383 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:03.350137 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:03.446077 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:03.564660 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:03.639935 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:03.717514 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:03.795187 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:03.797365 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:03.842127 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:03.888922 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:03.960295 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:03.999048 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.067062 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.069093 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.130516 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.177448 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.234817 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.245537 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_custom_fields".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.305623 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.318135 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_satisfaction_rating".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.395508 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.397401 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.480029 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.498049 (MainThread): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.587486 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.589407 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.657478 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.667757 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_to".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.755511 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.779447 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_via_source_from".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.799781 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.818689 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.909319 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:04.927950 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:05.025972 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:05.036138 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:05.098519 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:05.145555 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_scd".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:05.257737 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:05.301564 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:05.324003 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:05.332737 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:05.348247 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:05.349993 (MainThread): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:05.394399 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:05.400926 (MainThread): Acquiring new redshift connection "model.airbyte_utils.tickets_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:05.450819 (MainThread): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:05.891730 (MainThread): [WARNING]: Configuration paths exist in your dbt_project.yml file which do not apply to any resources.
2022-04-19 13:15:46 normalization > There are 1 unused configuration paths:
2022-04-19 13:15:46 normalization > - models.airbyte_utils.generated.airbyte_tables
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:05.967631 (MainThread): write_gpickle is deprecated and will be removed in 3.0.Use ``pickle.dump(G, path, protocol)``
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:05.985412 (MainThread): Found 104 models, 0 tests, 0 snapshots, 0 analyses, 520 macros, 0 operations, 0 seed files, 7 sources, 0 exposures
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.001517 (MainThread): 
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.004695 (MainThread): Acquiring new redshift connection "master".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.007945 (ThreadPoolExecutor-0_0): Acquiring new redshift connection "list_datalake".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.032701 (ThreadPoolExecutor-0_1): Acquiring new redshift connection "list_datalake".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.059307 (ThreadPoolExecutor-0_0): Using redshift connection "list_datalake".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.073336 (ThreadPoolExecutor-0_0): On list_datalake: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "connection_name": "list_datalake"} */
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     select distinct nspname from pg_namespace
2022-04-19 13:15:46 normalization >   
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.073512 (ThreadPoolExecutor-0_0): Opening a new connection, currently in state init
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.073634 (ThreadPoolExecutor-0_0): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.073076 (ThreadPoolExecutor-0_1): Using redshift connection "list_datalake".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.074673 (ThreadPoolExecutor-0_1): On list_datalake: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "connection_name": "list_datalake"} */
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     select distinct nspname from pg_namespace
2022-04-19 13:15:46 normalization >   
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.074830 (ThreadPoolExecutor-0_1): Opening a new connection, currently in state init
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.074947 (ThreadPoolExecutor-0_1): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.114088 (ThreadPoolExecutor-0_0): SQL status: SELECT in 0.04 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.116289 (ThreadPoolExecutor-0_0): On list_datalake: Close
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.117078 (ThreadPoolExecutor-0_1): SQL status: SELECT in 0.04 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.118730 (ThreadPoolExecutor-0_1): On list_datalake: Close
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.122668 (ThreadPoolExecutor-1_0): Acquiring new redshift connection "list_datalake_zendesk".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.145704 (ThreadPoolExecutor-1_1): Acquiring new redshift connection "list_datalake__airbyte_zendesk".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.161261 (ThreadPoolExecutor-1_1): Using redshift connection "list_datalake__airbyte_zendesk".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.161769 (ThreadPoolExecutor-1_1): On list_datalake__airbyte_zendesk: BEGIN
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.161925 (ThreadPoolExecutor-1_1): Opening a new connection, currently in state closed
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.162042 (ThreadPoolExecutor-1_1): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.162685 (ThreadPoolExecutor-1_0): Using redshift connection "list_datalake_zendesk".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.162831 (ThreadPoolExecutor-1_0): On list_datalake_zendesk: BEGIN
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.163145 (ThreadPoolExecutor-1_0): Opening a new connection, currently in state closed
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.163263 (ThreadPoolExecutor-1_0): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.192682 (ThreadPoolExecutor-1_1): SQL status: BEGIN in 0.03 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.192962 (ThreadPoolExecutor-1_1): Using redshift connection "list_datalake__airbyte_zendesk".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.193091 (ThreadPoolExecutor-1_1): On list_datalake__airbyte_zendesk: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "connection_name": "list_datalake__airbyte_zendesk"} */
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >       'datalake' as database,
2022-04-19 13:15:46 normalization >       tablename as name,
2022-04-19 13:15:46 normalization >       schemaname as schema,
2022-04-19 13:15:46 normalization >       'table' as type
2022-04-19 13:15:46 normalization >     from pg_tables
2022-04-19 13:15:46 normalization >     where schemaname ilike '_airbyte_zendesk'
2022-04-19 13:15:46 normalization >     union all
2022-04-19 13:15:46 normalization >     select
2022-04-19 13:15:46 normalization >       'datalake' as database,
2022-04-19 13:15:46 normalization >       viewname as name,
2022-04-19 13:15:46 normalization >       schemaname as schema,
2022-04-19 13:15:46 normalization >       'view' as type
2022-04-19 13:15:46 normalization >     from pg_views
2022-04-19 13:15:46 normalization >     where schemaname ilike '_airbyte_zendesk'
2022-04-19 13:15:46 normalization >   
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.193812 (ThreadPoolExecutor-1_0): SQL status: BEGIN in 0.03 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.193960 (ThreadPoolExecutor-1_0): Using redshift connection "list_datalake_zendesk".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.194074 (ThreadPoolExecutor-1_0): On list_datalake_zendesk: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "connection_name": "list_datalake_zendesk"} */
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >       'datalake' as database,
2022-04-19 13:15:46 normalization >       tablename as name,
2022-04-19 13:15:46 normalization >       schemaname as schema,
2022-04-19 13:15:46 normalization >       'table' as type
2022-04-19 13:15:46 normalization >     from pg_tables
2022-04-19 13:15:46 normalization >     where schemaname ilike 'zendesk'
2022-04-19 13:15:46 normalization >     union all
2022-04-19 13:15:46 normalization >     select
2022-04-19 13:15:46 normalization >       'datalake' as database,
2022-04-19 13:15:46 normalization >       viewname as name,
2022-04-19 13:15:46 normalization >       schemaname as schema,
2022-04-19 13:15:46 normalization >       'view' as type
2022-04-19 13:15:46 normalization >     from pg_views
2022-04-19 13:15:46 normalization >     where schemaname ilike 'zendesk'
2022-04-19 13:15:46 normalization >   
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.241074 (ThreadPoolExecutor-1_1): SQL status: SELECT in 0.05 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.242803 (ThreadPoolExecutor-1_1): On list_datalake__airbyte_zendesk: ROLLBACK
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.246398 (ThreadPoolExecutor-1_1): On list_datalake__airbyte_zendesk: Close
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.249568 (ThreadPoolExecutor-1_0): SQL status: SELECT in 0.06 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.253534 (ThreadPoolExecutor-1_0): On list_datalake_zendesk: ROLLBACK
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.257342 (ThreadPoolExecutor-1_0): On list_datalake_zendesk: Close
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.280725 (MainThread): Using redshift connection "master".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.280942 (MainThread): On master: BEGIN
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.281085 (MainThread): Opening a new connection, currently in state init
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.281200 (MainThread): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.308279 (MainThread): SQL status: BEGIN in 0.03 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.308573 (MainThread): Using redshift connection "master".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.308704 (MainThread): On master: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "connection_name": "master"} */
2022-04-19 13:15:46 normalization > with relation as (
2022-04-19 13:15:46 normalization >         select
2022-04-19 13:15:46 normalization >             pg_rewrite.ev_class as class,
2022-04-19 13:15:46 normalization >             pg_rewrite.oid as id
2022-04-19 13:15:46 normalization >         from pg_rewrite
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization >     class as (
2022-04-19 13:15:46 normalization >         select
2022-04-19 13:15:46 normalization >             oid as id,
2022-04-19 13:15:46 normalization >             relname as name,
2022-04-19 13:15:46 normalization >             relnamespace as schema,
2022-04-19 13:15:46 normalization >             relkind as kind
2022-04-19 13:15:46 normalization >         from pg_class
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization >     dependency as (
2022-04-19 13:15:46 normalization >         select
2022-04-19 13:15:46 normalization >             pg_depend.objid as id,
2022-04-19 13:15:46 normalization >             pg_depend.refobjid as ref
2022-04-19 13:15:46 normalization >         from pg_depend
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization >     schema as (
2022-04-19 13:15:46 normalization >         select
2022-04-19 13:15:46 normalization >             pg_namespace.oid as id,
2022-04-19 13:15:46 normalization >             pg_namespace.nspname as name
2022-04-19 13:15:46 normalization >         from pg_namespace
2022-04-19 13:15:46 normalization >         where nspname != 'information_schema' and nspname not like 'pg\_%'
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization >     referenced as (
2022-04-19 13:15:46 normalization >         select
2022-04-19 13:15:46 normalization >             relation.id AS id,
2022-04-19 13:15:46 normalization >             referenced_class.name ,
2022-04-19 13:15:46 normalization >             referenced_class.schema ,
2022-04-19 13:15:46 normalization >             referenced_class.kind
2022-04-19 13:15:46 normalization >         from relation
2022-04-19 13:15:46 normalization >         join class as referenced_class on relation.class=referenced_class.id
2022-04-19 13:15:46 normalization >         where referenced_class.kind in ('r', 'v')
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization >     relationships as (
2022-04-19 13:15:46 normalization >         select
2022-04-19 13:15:46 normalization >             referenced.name as referenced_name,
2022-04-19 13:15:46 normalization >             referenced.schema as referenced_schema_id,
2022-04-19 13:15:46 normalization >             dependent_class.name as dependent_name,
2022-04-19 13:15:46 normalization >             dependent_class.schema as dependent_schema_id,
2022-04-19 13:15:46 normalization >             referenced.kind as kind
2022-04-19 13:15:46 normalization >         from referenced
2022-04-19 13:15:46 normalization >         join dependency on referenced.id=dependency.id
2022-04-19 13:15:46 normalization >         join class as dependent_class on dependency.ref=dependent_class.id
2022-04-19 13:15:46 normalization >         where
2022-04-19 13:15:46 normalization >             (referenced.name != dependent_class.name or
2022-04-19 13:15:46 normalization >              referenced.schema != dependent_class.schema)
2022-04-19 13:15:46 normalization >     )
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     select
2022-04-19 13:15:46 normalization >         referenced_schema.name as referenced_schema,
2022-04-19 13:15:46 normalization >         relationships.referenced_name as referenced_name,
2022-04-19 13:15:46 normalization >         dependent_schema.name as dependent_schema,
2022-04-19 13:15:46 normalization >         relationships.dependent_name as dependent_name
2022-04-19 13:15:46 normalization >     from relationships
2022-04-19 13:15:46 normalization >     join schema as dependent_schema on relationships.dependent_schema_id=dependent_schema.id
2022-04-19 13:15:46 normalization >     join schema as referenced_schema on relationships.referenced_schema_id=referenced_schema.id
2022-04-19 13:15:46 normalization >     group by referenced_schema, referenced_name, dependent_schema, dependent_name
2022-04-19 13:15:46 normalization >     order by referenced_schema, referenced_name, dependent_schema, dependent_name;
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.378022 (MainThread): SQL status: SELECT in 0.07 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.384766 (MainThread): On master: ROLLBACK
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.387677 (MainThread): Using redshift connection "master".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.387835 (MainThread): On master: BEGIN
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.393120 (MainThread): SQL status: BEGIN in 0.01 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.393305 (MainThread): On master: COMMIT
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.393436 (MainThread): Using redshift connection "master".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.393553 (MainThread): On master: COMMIT
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.396059 (MainThread): SQL status: COMMIT in 0.00 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.396226 (MainThread): On master: Close
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.396899 (MainThread): 13:15:06 | Concurrency: 4 threads (target='prod')
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.397401 (MainThread): 13:15:06 | 
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.417670 (Thread-1): Began running node model.airbyte_utils.brands_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.418717 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.brands_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.418911 (Thread-1): Compiling model.airbyte_utils.brands_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.417971 (Thread-2): Began running node model.airbyte_utils.sla_policies_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.446479 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.sla_policies_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.446738 (Thread-2): Compiling model.airbyte_utils.sla_policies_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.460132 (Thread-3): Began running node model.airbyte_utils.tags_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.460661 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.tags_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.460833 (Thread-3): Compiling model.airbyte_utils.tags_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.491478 (Thread-4): Began running node model.airbyte_utils.ticket_fields_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.492142 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.492356 (Thread-4): Compiling model.airbyte_utils.ticket_fields_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.622787 (Thread-3): Writing injected SQL for node "model.airbyte_utils.tags_ab1"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.623935 (Thread-3): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.624514 (Thread-3): Finished running node model.airbyte_utils.tags_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.624735 (Thread-3): Began running node model.airbyte_utils.ticket_metric_events_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.625090 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.625255 (Thread-3): Compiling model.airbyte_utils.ticket_metric_events_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.651905 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.682000 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metric_events_ab1"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.715227 (Thread-3): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.715710 (Thread-3): Finished running node model.airbyte_utils.ticket_metric_events_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.715918 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.716290 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.716464 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.714761 (Thread-2): Writing injected SQL for node "model.airbyte_utils.sla_policies_ab1"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.771805 (Thread-2): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.772317 (Thread-2): Finished running node model.airbyte_utils.sla_policies_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.772523 (Thread-2): Began running node model.airbyte_utils.tickets_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.772875 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.tickets_ab1".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.773040 (Thread-2): Compiling model.airbyte_utils.tickets_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.771389 (Thread-1): Writing injected SQL for node "model.airbyte_utils.brands_ab1"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.796994 (Thread-1): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.797543 (Thread-1): Finished running node model.airbyte_utils.brands_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.752090 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_fields_ab1"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.803450 (Thread-4): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.803886 (Thread-4): Finished running node model.airbyte_utils.ticket_fields_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.804076 (Thread-4): Began running node model.airbyte_utils.ticket_metric_events_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.804779 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.804955 (Thread-4): Compiling model.airbyte_utils.ticket_metric_events_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.797754 (Thread-1): Began running node model.airbyte_utils.tags_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.825499 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.tags_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.825722 (Thread-1): Compiling model.airbyte_utils.tags_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.964038 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.985455 (Thread-1): Writing injected SQL for node "model.airbyte_utils.tags_ab2"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.985997 (Thread-1): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.986538 (Thread-1): Finished running node model.airbyte_utils.tags_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.986774 (Thread-1): Began running node model.airbyte_utils.sla_policies_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.996729 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.sla_policies_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.996941 (Thread-1): Compiling model.airbyte_utils.sla_policies_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.988974 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:06.992007 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metric_events_ab2"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.072672 (Thread-4): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.073232 (Thread-4): Finished running node model.airbyte_utils.ticket_metric_events_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.073437 (Thread-4): Began running node model.airbyte_utils.brands_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.073786 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.brands_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.073972 (Thread-4): Compiling model.airbyte_utils.brands_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.031609 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.090211 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.090966 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.091669 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.096494 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.117093 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.118648 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_ab1"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.119008 (Thread-3): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.119474 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.119700 (Thread-3): Began running node model.airbyte_utils.ticket_fields_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.155892 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.156092 (Thread-3): Compiling model.airbyte_utils.ticket_fields_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.140447 (Thread-1): Writing injected SQL for node "model.airbyte_utils.sla_policies_ab2"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.182279 (Thread-1): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.182765 (Thread-1): Finished running node model.airbyte_utils.sla_policies_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.182969 (Thread-1): Began running node model.airbyte_utils.tags_ab3
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.183324 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.tags_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.183486 (Thread-1): Compiling model.airbyte_utils.tags_ab3
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.181811 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.202685 (Thread-2): Writing injected SQL for node "model.airbyte_utils.tickets_ab1"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.220694 (Thread-2): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.221272 (Thread-2): Finished running node model.airbyte_utils.tickets_ab1
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.221485 (Thread-2): Began running node model.airbyte_utils.ticket_metric_events_stg
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.221865 (Thread-2): 13:15:07 | 1 of 33 START view model _airbyte_zendesk.ticket_metric_events_stg........................................... [RUN]
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.222444 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.222650 (Thread-2): Compiling model.airbyte_utils.ticket_metric_events_stg
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.263922 (Thread-1): Writing injected SQL for node "model.airbyte_utils.tags_ab3"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.332094 (Thread-1): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.332698 (Thread-1): Finished running node model.airbyte_utils.tags_ab3
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.332907 (Thread-1): Began running node model.airbyte_utils.ticket_metrics_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.333261 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.333426 (Thread-1): Compiling model.airbyte_utils.ticket_metrics_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.411302 (Thread-4): Writing injected SQL for node "model.airbyte_utils.brands_ab2"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.412573 (Thread-2): Writing injected SQL for node "model.airbyte_utils.ticket_metric_events_stg"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.413117 (Thread-2): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.431666 (Thread-4): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.432306 (Thread-4): Finished running node model.airbyte_utils.brands_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.432525 (Thread-4): Began running node model.airbyte_utils.sla_policies_ab3
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.432889 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.sla_policies_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.433057 (Thread-4): Compiling model.airbyte_utils.sla_policies_ab3
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.598488 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_fields_ab2"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.599005 (Thread-3): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.599569 (Thread-3): Finished running node model.airbyte_utils.ticket_fields_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.599811 (Thread-3): Began running node model.airbyte_utils.tickets_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.649692 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.tickets_ab2".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.649886 (Thread-3): Compiling model.airbyte_utils.tickets_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.649072 (Thread-4): Writing injected SQL for node "model.airbyte_utils.sla_policies_ab3"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.667680 (Thread-4): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.668177 (Thread-4): Finished running node model.airbyte_utils.sla_policies_ab3
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.676651 (Thread-4): Began running node model.airbyte_utils.tags
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.677042 (Thread-4): 13:15:07 | 2 of 33 START incremental model zendesk.tags................................................................. [RUN]
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.678078 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.tags".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.678264 (Thread-4): Compiling model.airbyte_utils.tags
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.667201 (Thread-2): Writing runtime SQL for node "model.airbyte_utils.ticket_metric_events_stg"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.721337 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.721554 (Thread-2): On model.airbyte_utils.ticket_metric_events_stg: BEGIN
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.721708 (Thread-2): Opening a new connection, currently in state closed
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.721837 (Thread-2): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.793045 (Thread-2): SQL status: BEGIN in 0.07 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.793326 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.793462 (Thread-2): On model.airbyte_utils.ticket_metric_events_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_stg"} */
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >   create view "datalake"._airbyte_zendesk."ticket_metric_events_stg__dbt_tmp" as (
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization > with __dbt__cte__ticket_metric_events_ab1 as (
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:46 normalization > -- depends_on: "datalake".zendesk._airbyte_raw_ticket_metric_events
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'id', true) != '' then json_extract_path_text(_airbyte_data, 'id', true) end as id,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'time', true) != '' then json_extract_path_text(_airbyte_data, 'time', true) end as "time",
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'type', true) != '' then json_extract_path_text(_airbyte_data, 'type', true) end as type,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'metric', true) != '' then json_extract_path_text(_airbyte_data, 'metric', true) end as metric,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'ticket_id', true) != '' then json_extract_path_text(_airbyte_data, 'ticket_id', true) end as ticket_id,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'instance_id', true) != '' then json_extract_path_text(_airbyte_data, 'instance_id', true) end as instance_id,
2022-04-19 13:15:46 normalization >     _airbyte_ab_id,
2022-04-19 13:15:46 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:46 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:46 normalization > from "datalake".zendesk._airbyte_raw_ticket_metric_events as table_alias
2022-04-19 13:15:46 normalization > -- ticket_metric_events
2022-04-19 13:15:46 normalization > where 1 = 1
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > ),  __dbt__cte__ticket_metric_events_ab2 as (
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:46 normalization > -- depends_on: __dbt__cte__ticket_metric_events_ab1
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >     cast(id as 
2022-04-19 13:15:46 normalization >     bigint
2022-04-19 13:15:46 normalization > ) as id,
2022-04-19 13:15:46 normalization >     cast("time" as varchar) as "time",
2022-04-19 13:15:46 normalization >     cast(type as varchar) as type,
2022-04-19 13:15:46 normalization >     cast(metric as varchar) as metric,
2022-04-19 13:15:46 normalization >     cast(ticket_id as 
2022-04-19 13:15:46 normalization >     bigint
2022-04-19 13:15:46 normalization > ) as ticket_id,
2022-04-19 13:15:46 normalization >     cast(instance_id as 
2022-04-19 13:15:46 normalization >     bigint
2022-04-19 13:15:46 normalization > ) as instance_id,
2022-04-19 13:15:46 normalization >     _airbyte_ab_id,
2022-04-19 13:15:46 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:46 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:46 normalization > from __dbt__cte__ticket_metric_events_ab1
2022-04-19 13:15:46 normalization > -- ticket_metric_events
2022-04-19 13:15:46 normalization > where 1 = 1
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > )-- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:46 normalization > -- depends_on: __dbt__cte__ticket_metric_events_ab2
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >     md5(cast(coalesce(cast(id as varchar), '') || '-' || coalesce(cast("time" as varchar), '') || '-' || coalesce(cast(type as varchar), '') || '-' || coalesce(cast(metric as varchar), '') || '-' || coalesce(cast(ticket_id as varchar), '') || '-' || coalesce(cast(instance_id as varchar), '') as varchar)) as _airbyte_ticket_metric_events_hashid,
2022-04-19 13:15:46 normalization >     tmp.*
2022-04-19 13:15:46 normalization > from __dbt__cte__ticket_metric_events_ab2 tmp
2022-04-19 13:15:46 normalization > -- ticket_metric_events
2022-04-19 13:15:46 normalization > where 1 = 1
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >   ) ;
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.807395 (Thread-1): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_ab2"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.828545 (Thread-1): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.829210 (Thread-1): Finished running node model.airbyte_utils.ticket_metrics_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.829428 (Thread-1): Began running node model.airbyte_utils.brands_ab3
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.829793 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.brands_ab3".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.829973 (Thread-1): Compiling model.airbyte_utils.brands_ab3
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.807671 (Thread-2): SQL status: CREATE VIEW in 0.01 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.913753 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.914016 (Thread-2): On model.airbyte_utils.ticket_metric_events_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_stg"} */
2022-04-19 13:15:46 normalization > alter table "datalake"._airbyte_zendesk."ticket_metric_events_stg__dbt_tmp" rename to "ticket_metric_events_stg"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.940223 (Thread-4): Using redshift connection "model.airbyte_utils.tags".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.940509 (Thread-4): On model.airbyte_utils.tags: BEGIN
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.940661 (Thread-4): Opening a new connection, currently in state init
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.940778 (Thread-4): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:07.946690 (Thread-2): SQL status: ALTER TABLE in 0.03 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.001705 (Thread-2): On model.airbyte_utils.ticket_metric_events_stg: COMMIT
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.001971 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.002102 (Thread-2): On model.airbyte_utils.ticket_metric_events_stg: COMMIT
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.029538 (Thread-4): SQL status: BEGIN in 0.09 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.029817 (Thread-4): Using redshift connection "model.airbyte_utils.tags".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.029949 (Thread-4): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >       with bound_views as (
2022-04-19 13:15:46 normalization >         select
2022-04-19 13:15:46 normalization >           ordinal_position,
2022-04-19 13:15:46 normalization >           table_schema,
2022-04-19 13:15:46 normalization >           column_name,
2022-04-19 13:15:46 normalization >           data_type,
2022-04-19 13:15:46 normalization >           character_maximum_length,
2022-04-19 13:15:46 normalization >           numeric_precision,
2022-04-19 13:15:46 normalization >           numeric_scale
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >         from information_schema."columns"
2022-04-19 13:15:46 normalization >         where table_name = 'tags'
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     unbound_views as (
2022-04-19 13:15:46 normalization >       select
2022-04-19 13:15:46 normalization >         ordinal_position,
2022-04-19 13:15:46 normalization >         view_schema,
2022-04-19 13:15:46 normalization >         col_name,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:46 normalization >             'character varying'
2022-04-19 13:15:46 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:46 normalization >           else col_type
2022-04-19 13:15:46 normalization >         end as col_type,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type like 'character%'
2022-04-19 13:15:46 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as character_maximum_length,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_precision,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_scale
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:46 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:46 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:46 normalization >       where view_name = 'tags'
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     external_views as (
2022-04-19 13:15:46 normalization >       select
2022-04-19 13:15:46 normalization >         columnnum,
2022-04-19 13:15:46 normalization >         schemaname,
2022-04-19 13:15:46 normalization >         columnname,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:46 normalization >           then 'character varying'
2022-04-19 13:15:46 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:46 normalization >           else external_type
2022-04-19 13:15:46 normalization >         end as external_type,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as character_maximum_length,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_precision,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_scale
2022-04-19 13:15:46 normalization >       from
2022-04-19 13:15:46 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:46 normalization >       where
2022-04-19 13:15:46 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:46 normalization >         and tablename = 'tags'
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     unioned as (
2022-04-19 13:15:46 normalization >       select * from bound_views
2022-04-19 13:15:46 normalization >       union all
2022-04-19 13:15:46 normalization >       select * from unbound_views
2022-04-19 13:15:46 normalization >       union all
2022-04-19 13:15:46 normalization >       select * from external_views
2022-04-19 13:15:46 normalization >     )
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     select
2022-04-19 13:15:46 normalization >       column_name,
2022-04-19 13:15:46 normalization >       data_type,
2022-04-19 13:15:46 normalization >       character_maximum_length,
2022-04-19 13:15:46 normalization >       numeric_precision,
2022-04-19 13:15:46 normalization >       numeric_scale
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     from unioned
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >     order by ordinal_position
2022-04-19 13:15:46 normalization >   
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.053299 (Thread-1): Writing injected SQL for node "model.airbyte_utils.brands_ab3"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.064964 (Thread-1): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.065544 (Thread-1): Finished running node model.airbyte_utils.brands_ab3
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.064497 (Thread-3): Writing injected SQL for node "model.airbyte_utils.tickets_ab2"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.066354 (Thread-3): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.066742 (Thread-3): Finished running node model.airbyte_utils.tickets_ab2
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.066940 (Thread-3): Began running node model.airbyte_utils.sla_policies
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.067444 (Thread-3): 13:15:08 | 4 of 33 START incremental model zendesk.sla_policies......................................................... [RUN]
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.065770 (Thread-1): Began running node model.airbyte_utils.ticket_fields_stg
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.068314 (Thread-1): 13:15:08 | 3 of 33 START view model _airbyte_zendesk.ticket_fields_stg.................................................. [RUN]
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.068680 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.sla_policies".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.068848 (Thread-3): Compiling model.airbyte_utils.sla_policies
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.076373 (Thread-2): SQL status: COMMIT in 0.07 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.076905 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.077048 (Thread-2): On model.airbyte_utils.ticket_metric_events_stg: BEGIN
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.077802 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.077964 (Thread-1): Compiling model.airbyte_utils.ticket_fields_stg
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.089386 (Thread-2): SQL status: BEGIN in 0.01 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.099997 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.100165 (Thread-2): On model.airbyte_utils.ticket_metric_events_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_stg"} */
2022-04-19 13:15:46 normalization > drop view if exists "datalake"._airbyte_zendesk."ticket_metric_events_stg__dbt_backup" cascade
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.122833 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.140568 (Thread-3): On model.airbyte_utils.sla_policies: BEGIN
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.140780 (Thread-3): Opening a new connection, currently in state init
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.140910 (Thread-3): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.166189 (Thread-2): SQL status: DROP VIEW in 0.03 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.167370 (Thread-2): On model.airbyte_utils.ticket_metric_events_stg: COMMIT
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.167525 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.167657 (Thread-2): On model.airbyte_utils.ticket_metric_events_stg: COMMIT
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.216779 (Thread-3): SQL status: BEGIN in 0.08 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.217838 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.218042 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >       with bound_views as (
2022-04-19 13:15:46 normalization >         select
2022-04-19 13:15:46 normalization >           ordinal_position,
2022-04-19 13:15:46 normalization >           table_schema,
2022-04-19 13:15:46 normalization >           column_name,
2022-04-19 13:15:46 normalization >           data_type,
2022-04-19 13:15:46 normalization >           character_maximum_length,
2022-04-19 13:15:46 normalization >           numeric_precision,
2022-04-19 13:15:46 normalization >           numeric_scale
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >         from information_schema."columns"
2022-04-19 13:15:46 normalization >         where table_name = 'sla_policies'
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     unbound_views as (
2022-04-19 13:15:46 normalization >       select
2022-04-19 13:15:46 normalization >         ordinal_position,
2022-04-19 13:15:46 normalization >         view_schema,
2022-04-19 13:15:46 normalization >         col_name,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:46 normalization >             'character varying'
2022-04-19 13:15:46 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:46 normalization >           else col_type
2022-04-19 13:15:46 normalization >         end as col_type,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type like 'character%'
2022-04-19 13:15:46 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as character_maximum_length,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_precision,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_scale
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:46 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:46 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:46 normalization >       where view_name = 'sla_policies'
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     external_views as (
2022-04-19 13:15:46 normalization >       select
2022-04-19 13:15:46 normalization >         columnnum,
2022-04-19 13:15:46 normalization >         schemaname,
2022-04-19 13:15:46 normalization >         columnname,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:46 normalization >           then 'character varying'
2022-04-19 13:15:46 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:46 normalization >           else external_type
2022-04-19 13:15:46 normalization >         end as external_type,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as character_maximum_length,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_precision,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_scale
2022-04-19 13:15:46 normalization >       from
2022-04-19 13:15:46 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:46 normalization >       where
2022-04-19 13:15:46 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:46 normalization >         and tablename = 'sla_policies'
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     unioned as (
2022-04-19 13:15:46 normalization >       select * from bound_views
2022-04-19 13:15:46 normalization >       union all
2022-04-19 13:15:46 normalization >       select * from unbound_views
2022-04-19 13:15:46 normalization >       union all
2022-04-19 13:15:46 normalization >       select * from external_views
2022-04-19 13:15:46 normalization >     )
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     select
2022-04-19 13:15:46 normalization >       column_name,
2022-04-19 13:15:46 normalization >       data_type,
2022-04-19 13:15:46 normalization >       character_maximum_length,
2022-04-19 13:15:46 normalization >       numeric_precision,
2022-04-19 13:15:46 normalization >       numeric_scale
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     from unioned
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >     order by ordinal_position
2022-04-19 13:15:46 normalization >   
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.244342 (Thread-4): SQL status: SELECT in 0.21 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.254035 (Thread-2): SQL status: COMMIT in 0.09 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.256523 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.256670 (Thread-2): On model.airbyte_utils.ticket_metric_events_stg: BEGIN
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.274273 (Thread-2): SQL status: BEGIN in 0.02 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.275617 (Thread-2): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.276247 (Thread-2): On model.airbyte_utils.ticket_metric_events_stg: ROLLBACK
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.288371 (Thread-2): On model.airbyte_utils.ticket_metric_events_stg: Close
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.289362 (Thread-2): 13:15:08 | 1 of 33 OK created view model _airbyte_zendesk.ticket_metric_events_stg...................................... [CREATE VIEW in 1.07s]
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.296408 (Thread-2): Finished running node model.airbyte_utils.ticket_metric_events_stg
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.296676 (Thread-2): Began running node model.airbyte_utils.ticket_metrics_stg
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.297297 (Thread-2): 13:15:08 | 5 of 33 START view model _airbyte_zendesk.ticket_metrics_stg................................................. [RUN]
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.298179 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.298364 (Thread-2): Compiling model.airbyte_utils.ticket_metrics_stg
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.472523 (Thread-4): Writing injected SQL for node "model.airbyte_utils.tags"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.497398 (Thread-3): SQL status: SELECT in 0.28 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.498030 (Thread-4): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.570720 (Thread-3): Writing injected SQL for node "model.airbyte_utils.sla_policies"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.633933 (Thread-3): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.678368 (Thread-1): Writing injected SQL for node "model.airbyte_utils.ticket_fields_stg"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.764065 (Thread-1): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.774774 (Thread-1): Writing runtime SQL for node "model.airbyte_utils.ticket_fields_stg"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.831934 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.832156 (Thread-1): On model.airbyte_utils.ticket_fields_stg: BEGIN
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.832331 (Thread-1): Opening a new connection, currently in state closed
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.832455 (Thread-1): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.904486 (Thread-4): Using redshift connection "model.airbyte_utils.tags".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.912733 (Thread-1): SQL status: BEGIN in 0.08 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.921854 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.921975 (Thread-1): On model.airbyte_utils.ticket_fields_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_stg"} */
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >   create view "datalake"._airbyte_zendesk."ticket_fields_stg__dbt_tmp" as (
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization > with __dbt__cte__ticket_fields_ab1 as (
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:46 normalization > -- depends_on: "datalake".zendesk._airbyte_raw_ticket_fields
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'id', true) != '' then json_extract_path_text(_airbyte_data, 'id', true) end as id,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'tag', true) != '' then json_extract_path_text(_airbyte_data, 'tag', true) end as "tag",
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'url', true) != '' then json_extract_path_text(_airbyte_data, 'url', true) end as url,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'type', true) != '' then json_extract_path_text(_airbyte_data, 'type', true) end as type,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'title', true) != '' then json_extract_path_text(_airbyte_data, 'title', true) end as title,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'active', true) != '' then json_extract_path_text(_airbyte_data, 'active', true) end as active,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'position', true) != '' then json_extract_path_text(_airbyte_data, 'position', true) end as position,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'required', true) != '' then json_extract_path_text(_airbyte_data, 'required', true) end as required,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'raw_title', true) != '' then json_extract_path_text(_airbyte_data, 'raw_title', true) end as raw_title,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'removable', true) != '' then json_extract_path_text(_airbyte_data, 'removable', true) end as removable,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'created_at', true) != '' then json_extract_path_text(_airbyte_data, 'created_at', true) end as created_at,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'updated_at', true) != '' then json_extract_path_text(_airbyte_data, 'updated_at', true) end as updated_at,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'description', true) != '' then json_extract_path_text(_airbyte_data, 'description', true) end as description,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'sub_type_id', true) != '' then json_extract_path_text(_airbyte_data, 'sub_type_id', true) end as sub_type_id,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'raw_description', true) != '' then json_extract_path_text(_airbyte_data, 'raw_description', true) end as raw_description,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'title_in_portal', true) != '' then json_extract_path_text(_airbyte_data, 'title_in_portal', true) end as title_in_portal,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'agent_description', true) != '' then json_extract_path_text(_airbyte_data, 'agent_description', true) end as agent_description,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'visible_in_portal', true) != '' then json_extract_path_text(_airbyte_data, 'visible_in_portal', true) end as visible_in_portal,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'editable_in_portal', true) != '' then json_extract_path_text(_airbyte_data, 'editable_in_portal', true) end as editable_in_portal,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'required_in_portal', true) != '' then json_extract_path_text(_airbyte_data, 'required_in_portal', true) end as required_in_portal,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'raw_title_in_portal', true) != '' then json_extract_path_text(_airbyte_data, 'raw_title_in_portal', true) end as raw_title_in_portal,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'collapsed_for_agents', true) != '' then json_extract_path_text(_airbyte_data, 'collapsed_for_agents', true) end as collapsed_for_agents,
2022-04-19 13:15:46 normalization >     json_extract_path_text(_airbyte_data, 'custom_field_options', true) as custom_field_options,
2022-04-19 13:15:46 normalization >     json_extract_path_text(_airbyte_data, 'system_field_options', true) as system_field_options,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'regexp_for_validation', true) != '' then json_extract_path_text(_airbyte_data, 'regexp_for_validation', true) end as regexp_for_validation,
2022-04-19 13:15:46 normalization >     _airbyte_ab_id,
2022-04-19 13:15:46 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:46 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:46 normalization > from "datalake".zendesk._airbyte_raw_ticket_fields as table_alias
2022-04-19 13:15:46 normalization > -- ticket_fields
2022-04-19 13:15:46 normalization > where 1 = 1
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > ),  __dbt__cte__ticket_fields_ab2 as (
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:46 normalization > -- depends_on: __dbt__cte__ticket_fields_ab1
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >     cast(id as 
2022-04-19 13:15:46 normalization >     bigint
2022-04-19 13:15:46 normalization > ) as id,
2022-04-19 13:15:46 normalization >     cast("tag" as varchar) as "tag",
2022-04-19 13:15:46 normalization >     cast(url as varchar) as url,
2022-04-19 13:15:46 normalization >     cast(type as varchar) as type,
2022-04-19 13:15:46 normalization >     cast(title as varchar) as title,
2022-04-19 13:15:46 normalization >     cast(decode(active, 'true', '1', 'false', '0')::integer as boolean) as active,
2022-04-19 13:15:46 normalization >     cast(position as 
2022-04-19 13:15:46 normalization >     bigint
2022-04-19 13:15:46 normalization > ) as position,
2022-04-19 13:15:46 normalization >     cast(decode(required, 'true', '1', 'false', '0')::integer as boolean) as required,
2022-04-19 13:15:46 normalization >     cast(raw_title as varchar) as raw_title,
2022-04-19 13:15:46 normalization >     cast(decode(removable, 'true', '1', 'false', '0')::integer as boolean) as removable,
2022-04-19 13:15:46 normalization >     cast(nullif(created_at, '') as 
2022-04-19 13:15:46 normalization >     timestamp with time zone
2022-04-19 13:15:46 normalization > ) as created_at,
2022-04-19 13:15:46 normalization >     cast(nullif(updated_at, '') as 
2022-04-19 13:15:46 normalization >     timestamp with time zone
2022-04-19 13:15:46 normalization > ) as updated_at,
2022-04-19 13:15:46 normalization >     cast(description as varchar) as description,
2022-04-19 13:15:46 normalization >     cast(sub_type_id as 
2022-04-19 13:15:46 normalization >     bigint
2022-04-19 13:15:46 normalization > ) as sub_type_id,
2022-04-19 13:15:46 normalization >     cast(raw_description as varchar) as raw_description,
2022-04-19 13:15:46 normalization >     cast(title_in_portal as varchar) as title_in_portal,
2022-04-19 13:15:46 normalization >     cast(agent_description as varchar) as agent_description,
2022-04-19 13:15:46 normalization >     cast(decode(visible_in_portal, 'true', '1', 'false', '0')::integer as boolean) as visible_in_portal,
2022-04-19 13:15:46 normalization >     cast(decode(editable_in_portal, 'true', '1', 'false', '0')::integer as boolean) as editable_in_portal,
2022-04-19 13:15:46 normalization >     cast(decode(required_in_portal, 'true', '1', 'false', '0')::integer as boolean) as required_in_portal,
2022-04-19 13:15:46 normalization >     cast(raw_title_in_portal as varchar) as raw_title_in_portal,
2022-04-19 13:15:46 normalization >     cast(decode(collapsed_for_agents, 'true', '1', 'false', '0')::integer as boolean) as collapsed_for_agents,
2022-04-19 13:15:46 normalization >     custom_field_options,
2022-04-19 13:15:46 normalization >     system_field_options,
2022-04-19 13:15:46 normalization >     cast(regexp_for_validation as varchar) as regexp_for_validation,
2022-04-19 13:15:46 normalization >     _airbyte_ab_id,
2022-04-19 13:15:46 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:46 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:46 normalization > from __dbt__cte__ticket_fields_ab1
2022-04-19 13:15:46 normalization > -- ticket_fields
2022-04-19 13:15:46 normalization > where 1 = 1
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > )-- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:46 normalization > -- depends_on: __dbt__cte__ticket_fields_ab2
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >     md5(cast(coalesce(cast(id as varchar), '') || '-' || coalesce(cast("tag" as varchar), '') || '-' || coalesce(cast(url as varchar), '') || '-' || coalesce(cast(type as varchar), '') || '-' || coalesce(cast(title as varchar), '') || '-' || coalesce(cast(case when active then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(position as varchar), '') || '-' || coalesce(cast(case when required then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(raw_title as varchar), '') || '-' || coalesce(cast(case when removable then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(created_at as varchar), '') || '-' || coalesce(cast(updated_at as varchar), '') || '-' || coalesce(cast(description as varchar), '') || '-' || coalesce(cast(sub_type_id as varchar), '') || '-' || coalesce(cast(raw_description as varchar), '') || '-' || coalesce(cast(title_in_portal as varchar), '') || '-' || coalesce(cast(agent_description as varchar), '') || '-' || coalesce(cast(case when visible_in_portal then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(case when editable_in_portal then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(case when required_in_portal then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(raw_title_in_portal as varchar), '') || '-' || coalesce(cast(case when collapsed_for_agents then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(custom_field_options as varchar), '') || '-' || coalesce(cast(system_field_options as varchar), '') || '-' || coalesce(cast(regexp_for_validation as varchar), '') as varchar)) as _airbyte_ticket_fields_hashid,
2022-04-19 13:15:46 normalization >     tmp.*
2022-04-19 13:15:46 normalization > from __dbt__cte__ticket_fields_ab2 tmp
2022-04-19 13:15:46 normalization > -- ticket_fields
2022-04-19 13:15:46 normalization > where 1 = 1
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >   ) ;
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.921691 (Thread-4): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >       with bound_views as (
2022-04-19 13:15:46 normalization >         select
2022-04-19 13:15:46 normalization >           ordinal_position,
2022-04-19 13:15:46 normalization >           table_schema,
2022-04-19 13:15:46 normalization >           column_name,
2022-04-19 13:15:46 normalization >           data_type,
2022-04-19 13:15:46 normalization >           character_maximum_length,
2022-04-19 13:15:46 normalization >           numeric_precision,
2022-04-19 13:15:46 normalization >           numeric_scale
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >         from information_schema."columns"
2022-04-19 13:15:46 normalization >         where table_name = 'tags'
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     unbound_views as (
2022-04-19 13:15:46 normalization >       select
2022-04-19 13:15:46 normalization >         ordinal_position,
2022-04-19 13:15:46 normalization >         view_schema,
2022-04-19 13:15:46 normalization >         col_name,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:46 normalization >             'character varying'
2022-04-19 13:15:46 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:46 normalization >           else col_type
2022-04-19 13:15:46 normalization >         end as col_type,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type like 'character%'
2022-04-19 13:15:46 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as character_maximum_length,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_precision,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_scale
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:46 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:46 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:46 normalization >       where view_name = 'tags'
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     external_views as (
2022-04-19 13:15:46 normalization >       select
2022-04-19 13:15:46 normalization >         columnnum,
2022-04-19 13:15:46 normalization >         schemaname,
2022-04-19 13:15:46 normalization >         columnname,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:46 normalization >           then 'character varying'
2022-04-19 13:15:46 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:46 normalization >           else external_type
2022-04-19 13:15:46 normalization >         end as external_type,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as character_maximum_length,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_precision,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_scale
2022-04-19 13:15:46 normalization >       from
2022-04-19 13:15:46 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:46 normalization >       where
2022-04-19 13:15:46 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:46 normalization >         and tablename = 'tags'
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     unioned as (
2022-04-19 13:15:46 normalization >       select * from bound_views
2022-04-19 13:15:46 normalization >       union all
2022-04-19 13:15:46 normalization >       select * from unbound_views
2022-04-19 13:15:46 normalization >       union all
2022-04-19 13:15:46 normalization >       select * from external_views
2022-04-19 13:15:46 normalization >     )
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     select
2022-04-19 13:15:46 normalization >       column_name,
2022-04-19 13:15:46 normalization >       data_type,
2022-04-19 13:15:46 normalization >       character_maximum_length,
2022-04-19 13:15:46 normalization >       numeric_precision,
2022-04-19 13:15:46 normalization >       numeric_scale
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     from unioned
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >     order by ordinal_position
2022-04-19 13:15:46 normalization >   
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.921478 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.922341 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >       with bound_views as (
2022-04-19 13:15:46 normalization >         select
2022-04-19 13:15:46 normalization >           ordinal_position,
2022-04-19 13:15:46 normalization >           table_schema,
2022-04-19 13:15:46 normalization >           column_name,
2022-04-19 13:15:46 normalization >           data_type,
2022-04-19 13:15:46 normalization >           character_maximum_length,
2022-04-19 13:15:46 normalization >           numeric_precision,
2022-04-19 13:15:46 normalization >           numeric_scale
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >         from information_schema."columns"
2022-04-19 13:15:46 normalization >         where table_name = 'sla_policies'
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     unbound_views as (
2022-04-19 13:15:46 normalization >       select
2022-04-19 13:15:46 normalization >         ordinal_position,
2022-04-19 13:15:46 normalization >         view_schema,
2022-04-19 13:15:46 normalization >         col_name,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:46 normalization >             'character varying'
2022-04-19 13:15:46 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:46 normalization >           else col_type
2022-04-19 13:15:46 normalization >         end as col_type,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type like 'character%'
2022-04-19 13:15:46 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as character_maximum_length,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_precision,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_scale
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:46 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:46 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:46 normalization >       where view_name = 'sla_policies'
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     external_views as (
2022-04-19 13:15:46 normalization >       select
2022-04-19 13:15:46 normalization >         columnnum,
2022-04-19 13:15:46 normalization >         schemaname,
2022-04-19 13:15:46 normalization >         columnname,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:46 normalization >           then 'character varying'
2022-04-19 13:15:46 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:46 normalization >           else external_type
2022-04-19 13:15:46 normalization >         end as external_type,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as character_maximum_length,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_precision,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_scale
2022-04-19 13:15:46 normalization >       from
2022-04-19 13:15:46 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:46 normalization >       where
2022-04-19 13:15:46 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:46 normalization >         and tablename = 'sla_policies'
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     unioned as (
2022-04-19 13:15:46 normalization >       select * from bound_views
2022-04-19 13:15:46 normalization >       union all
2022-04-19 13:15:46 normalization >       select * from unbound_views
2022-04-19 13:15:46 normalization >       union all
2022-04-19 13:15:46 normalization >       select * from external_views
2022-04-19 13:15:46 normalization >     )
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     select
2022-04-19 13:15:46 normalization >       column_name,
2022-04-19 13:15:46 normalization >       data_type,
2022-04-19 13:15:46 normalization >       character_maximum_length,
2022-04-19 13:15:46 normalization >       numeric_precision,
2022-04-19 13:15:46 normalization >       numeric_scale
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     from unioned
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >     order by ordinal_position
2022-04-19 13:15:46 normalization >   
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.947848 (Thread-2): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_stg"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.948303 (Thread-2): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.958591 (Thread-2): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics_stg"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.959031 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.959208 (Thread-2): On model.airbyte_utils.ticket_metrics_stg: BEGIN
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.959403 (Thread-2): Opening a new connection, currently in state closed
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.959524 (Thread-2): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.973837 (Thread-1): SQL status: CREATE VIEW in 0.05 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.977864 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.978033 (Thread-1): On model.airbyte_utils.ticket_fields_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_stg"} */
2022-04-19 13:15:46 normalization > alter table "datalake"._airbyte_zendesk."ticket_fields_stg__dbt_tmp" rename to "ticket_fields_stg"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.987936 (Thread-1): SQL status: ALTER TABLE in 0.01 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.989818 (Thread-1): On model.airbyte_utils.ticket_fields_stg: COMMIT
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.989981 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:08.990119 (Thread-1): On model.airbyte_utils.ticket_fields_stg: COMMIT
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.019571 (Thread-2): SQL status: BEGIN in 0.06 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.019876 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.020026 (Thread-2): On model.airbyte_utils.ticket_metrics_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_stg"} */
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >   create view "datalake"._airbyte_zendesk."ticket_metrics_stg__dbt_tmp" as (
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization > with __dbt__cte__ticket_metrics_ab1 as (
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:46 normalization > -- depends_on: "datalake".zendesk._airbyte_raw_ticket_metrics
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'id', true) != '' then json_extract_path_text(_airbyte_data, 'id', true) end as id,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'url', true) != '' then json_extract_path_text(_airbyte_data, 'url', true) end as url,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'time', true) != '' then json_extract_path_text(_airbyte_data, 'time', true) end as "time",
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'type', true) != '' then json_extract_path_text(_airbyte_data, 'type', true) end as type,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'metric', true) != '' then json_extract_path_text(_airbyte_data, 'metric', true) end as metric,
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >         case when json_extract_path_text(table_alias._airbyte_data, 'status', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'status', true) end
2022-04-19 13:15:46 normalization >      as status,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'reopens', true) != '' then json_extract_path_text(_airbyte_data, 'reopens', true) end as reopens,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'replies', true) != '' then json_extract_path_text(_airbyte_data, 'replies', true) end as replies,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'solved_at', true) != '' then json_extract_path_text(_airbyte_data, 'solved_at', true) end as solved_at,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'ticket_id', true) != '' then json_extract_path_text(_airbyte_data, 'ticket_id', true) end as ticket_id,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'created_at', true) != '' then json_extract_path_text(_airbyte_data, 'created_at', true) end as created_at,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'updated_at', true) != '' then json_extract_path_text(_airbyte_data, 'updated_at', true) end as updated_at,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'assigned_at', true) != '' then json_extract_path_text(_airbyte_data, 'assigned_at', true) end as assigned_at,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'instance_id', true) != '' then json_extract_path_text(_airbyte_data, 'instance_id', true) end as instance_id,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'group_stations', true) != '' then json_extract_path_text(_airbyte_data, 'group_stations', true) end as group_stations,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'assignee_stations', true) != '' then json_extract_path_text(_airbyte_data, 'assignee_stations', true) end as assignee_stations,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'status_updated_at', true) != '' then json_extract_path_text(_airbyte_data, 'status_updated_at', true) end as status_updated_at,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'assignee_updated_at', true) != '' then json_extract_path_text(_airbyte_data, 'assignee_updated_at', true) end as assignee_updated_at,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'requester_updated_at', true) != '' then json_extract_path_text(_airbyte_data, 'requester_updated_at', true) end as requester_updated_at,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'initially_assigned_at', true) != '' then json_extract_path_text(_airbyte_data, 'initially_assigned_at', true) end as initially_assigned_at,
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >         case when json_extract_path_text(table_alias._airbyte_data, 'reply_time_in_minutes', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'reply_time_in_minutes', true) end
2022-04-19 13:15:46 normalization >      as reply_time_in_minutes,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'latest_comment_added_at', true) != '' then json_extract_path_text(_airbyte_data, 'latest_comment_added_at', true) end as latest_comment_added_at,
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >         case when json_extract_path_text(table_alias._airbyte_data, 'on_hold_time_in_minutes', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'on_hold_time_in_minutes', true) end
2022-04-19 13:15:46 normalization >      as on_hold_time_in_minutes,
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >         case when json_extract_path_text(table_alias._airbyte_data, 'agent_wait_time_in_minutes', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'agent_wait_time_in_minutes', true) end
2022-04-19 13:15:46 normalization >      as agent_wait_time_in_minutes,
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >         case when json_extract_path_text(table_alias._airbyte_data, 'requester_wait_time_in_minutes', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'requester_wait_time_in_minutes', true) end
2022-04-19 13:15:46 normalization >      as requester_wait_time_in_minutes,
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >         case when json_extract_path_text(table_alias._airbyte_data, 'full_resolution_time_in_minutes', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'full_resolution_time_in_minutes', true) end
2022-04-19 13:15:46 normalization >      as full_resolution_time_in_minutes,
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >         case when json_extract_path_text(table_alias._airbyte_data, 'first_resolution_time_in_minutes', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'first_resolution_time_in_minutes', true) end
2022-04-19 13:15:46 normalization >      as first_resolution_time_in_minutes,
2022-04-19 13:15:46 normalization >     _airbyte_ab_id,
2022-04-19 13:15:46 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:46 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:46 normalization > from "datalake".zendesk._airbyte_raw_ticket_metrics as table_alias
2022-04-19 13:15:46 normalization > -- ticket_metrics
2022-04-19 13:15:46 normalization > where 1 = 1
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > ),  __dbt__cte__ticket_metrics_ab2 as (
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:46 normalization > -- depends_on: __dbt__cte__ticket_metrics_ab1
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >     cast(id as 
2022-04-19 13:15:46 normalization >     bigint
2022-04-19 13:15:46 normalization > ) as id,
2022-04-19 13:15:46 normalization >     cast(url as varchar) as url,
2022-04-19 13:15:46 normalization >     cast("time" as varchar) as "time",
2022-04-19 13:15:46 normalization >     cast(type as varchar) as type,
2022-04-19 13:15:46 normalization >     cast(metric as varchar) as metric,
2022-04-19 13:15:46 normalization >     cast(status as varchar) as status,
2022-04-19 13:15:46 normalization >     cast(reopens as 
2022-04-19 13:15:46 normalization >     bigint
2022-04-19 13:15:46 normalization > ) as reopens,
2022-04-19 13:15:46 normalization >     cast(replies as 
2022-04-19 13:15:46 normalization >     bigint
2022-04-19 13:15:46 normalization > ) as replies,
2022-04-19 13:15:46 normalization >     cast(nullif(solved_at, '') as 
2022-04-19 13:15:46 normalization >     timestamp with time zone
2022-04-19 13:15:46 normalization > ) as solved_at,
2022-04-19 13:15:46 normalization >     cast(ticket_id as 
2022-04-19 13:15:46 normalization >     bigint
2022-04-19 13:15:46 normalization > ) as ticket_id,
2022-04-19 13:15:46 normalization >     cast(nullif(created_at, '') as 
2022-04-19 13:15:46 normalization >     timestamp with time zone
2022-04-19 13:15:46 normalization > ) as created_at,
2022-04-19 13:15:46 normalization >     cast(nullif(updated_at, '') as 
2022-04-19 13:15:46 normalization >     timestamp with time zone
2022-04-19 13:15:46 normalization > ) as updated_at,
2022-04-19 13:15:46 normalization >     cast(nullif(assigned_at, '') as 
2022-04-19 13:15:46 normalization >     timestamp with time zone
2022-04-19 13:15:46 normalization > ) as assigned_at,
2022-04-19 13:15:46 normalization >     cast(instance_id as 
2022-04-19 13:15:46 normalization >     bigint
2022-04-19 13:15:46 normalization > ) as instance_id,
2022-04-19 13:15:46 normalization >     cast(group_stations as 
2022-04-19 13:15:46 normalization >     bigint
2022-04-19 13:15:46 normalization > ) as group_stations,
2022-04-19 13:15:46 normalization >     cast(assignee_stations as 
2022-04-19 13:15:46 normalization >     bigint
2022-04-19 13:15:46 normalization > ) as assignee_stations,
2022-04-19 13:15:46 normalization >     cast(nullif(status_updated_at, '') as 
2022-04-19 13:15:46 normalization >     timestamp with time zone
2022-04-19 13:15:46 normalization > ) as status_updated_at,
2022-04-19 13:15:46 normalization >     cast(nullif(assignee_updated_at, '') as 
2022-04-19 13:15:46 normalization >     timestamp with time zone
2022-04-19 13:15:46 normalization > ) as assignee_updated_at,
2022-04-19 13:15:46 normalization >     cast(nullif(requester_updated_at, '') as 
2022-04-19 13:15:46 normalization >     timestamp with time zone
2022-04-19 13:15:46 normalization > ) as requester_updated_at,
2022-04-19 13:15:46 normalization >     cast(nullif(initially_assigned_at, '') as 
2022-04-19 13:15:46 normalization >     timestamp with time zone
2022-04-19 13:15:46 normalization > ) as initially_assigned_at,
2022-04-19 13:15:46 normalization >     cast(reply_time_in_minutes as varchar) as reply_time_in_minutes,
2022-04-19 13:15:46 normalization >     cast(nullif(latest_comment_added_at, '') as 
2022-04-19 13:15:46 normalization >     timestamp with time zone
2022-04-19 13:15:46 normalization > ) as latest_comment_added_at,
2022-04-19 13:15:46 normalization >     cast(on_hold_time_in_minutes as varchar) as on_hold_time_in_minutes,
2022-04-19 13:15:46 normalization >     cast(agent_wait_time_in_minutes as varchar) as agent_wait_time_in_minutes,
2022-04-19 13:15:46 normalization >     cast(requester_wait_time_in_minutes as varchar) as requester_wait_time_in_minutes,
2022-04-19 13:15:46 normalization >     cast(full_resolution_time_in_minutes as varchar) as full_resolution_time_in_minutes,
2022-04-19 13:15:46 normalization >     cast(first_resolution_time_in_minutes as varchar) as first_resolution_time_in_minutes,
2022-04-19 13:15:46 normalization >     _airbyte_ab_id,
2022-04-19 13:15:46 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:46 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:46 normalization > from __dbt__cte__ticket_metrics_ab1
2022-04-19 13:15:46 normalization > -- ticket_metrics
2022-04-19 13:15:46 normalization > where 1 = 1
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > )-- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:46 normalization > -- depends_on: __dbt__cte__ticket_metrics_ab2
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >     md5(cast(coalesce(cast(id as varchar), '') || '-' || coalesce(cast(url as varchar), '') || '-' || coalesce(cast("time" as varchar), '') || '-' || coalesce(cast(type as varchar), '') || '-' || coalesce(cast(metric as varchar), '') || '-' || coalesce(cast(status as varchar), '') || '-' || coalesce(cast(reopens as varchar), '') || '-' || coalesce(cast(replies as varchar), '') || '-' || coalesce(cast(solved_at as varchar), '') || '-' || coalesce(cast(ticket_id as varchar), '') || '-' || coalesce(cast(created_at as varchar), '') || '-' || coalesce(cast(updated_at as varchar), '') || '-' || coalesce(cast(assigned_at as varchar), '') || '-' || coalesce(cast(instance_id as varchar), '') || '-' || coalesce(cast(group_stations as varchar), '') || '-' || coalesce(cast(assignee_stations as varchar), '') || '-' || coalesce(cast(status_updated_at as varchar), '') || '-' || coalesce(cast(assignee_updated_at as varchar), '') || '-' || coalesce(cast(requester_updated_at as varchar), '') || '-' || coalesce(cast(initially_assigned_at as varchar), '') || '-' || coalesce(cast(reply_time_in_minutes as varchar), '') || '-' || coalesce(cast(latest_comment_added_at as varchar), '') || '-' || coalesce(cast(on_hold_time_in_minutes as varchar), '') || '-' || coalesce(cast(agent_wait_time_in_minutes as varchar), '') || '-' || coalesce(cast(requester_wait_time_in_minutes as varchar), '') || '-' || coalesce(cast(full_resolution_time_in_minutes as varchar), '') || '-' || coalesce(cast(first_resolution_time_in_minutes as varchar), '') as varchar)) as _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:46 normalization >     tmp.*
2022-04-19 13:15:46 normalization > from __dbt__cte__ticket_metrics_ab2 tmp
2022-04-19 13:15:46 normalization > -- ticket_metrics
2022-04-19 13:15:46 normalization > where 1 = 1
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >   ) ;
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.082140 (Thread-1): SQL status: COMMIT in 0.09 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.082720 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.082865 (Thread-1): On model.airbyte_utils.ticket_fields_stg: BEGIN
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.086999 (Thread-1): SQL status: BEGIN in 0.00 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.089972 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.090129 (Thread-1): On model.airbyte_utils.ticket_fields_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_stg"} */
2022-04-19 13:15:46 normalization > drop view if exists "datalake"._airbyte_zendesk."ticket_fields_stg__dbt_backup" cascade
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.094191 (Thread-1): SQL status: DROP VIEW in 0.00 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.095222 (Thread-1): On model.airbyte_utils.ticket_fields_stg: COMMIT
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.095509 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.095691 (Thread-1): On model.airbyte_utils.ticket_fields_stg: COMMIT
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.117438 (Thread-2): SQL status: CREATE VIEW in 0.10 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.121317 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.121481 (Thread-2): On model.airbyte_utils.ticket_metrics_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_stg"} */
2022-04-19 13:15:46 normalization > alter table "datalake"._airbyte_zendesk."ticket_metrics_stg__dbt_tmp" rename to "ticket_metrics_stg"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.130366 (Thread-2): SQL status: ALTER TABLE in 0.01 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.131752 (Thread-2): On model.airbyte_utils.ticket_metrics_stg: COMMIT
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.131912 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.132034 (Thread-2): On model.airbyte_utils.ticket_metrics_stg: COMMIT
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.227167 (Thread-4): SQL status: SELECT in 0.30 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.268408 (Thread-1): SQL status: COMMIT in 0.17 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.268744 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.268887 (Thread-1): On model.airbyte_utils.ticket_fields_stg: BEGIN
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.269129 (Thread-3): SQL status: SELECT in 0.35 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.284421 (Thread-2): SQL status: COMMIT in 0.15 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.284867 (Thread-1): SQL status: BEGIN in 0.02 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.285466 (Thread-1): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.285668 (Thread-1): On model.airbyte_utils.ticket_fields_stg: ROLLBACK
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.285878 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.286016 (Thread-2): On model.airbyte_utils.ticket_metrics_stg: BEGIN
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.307784 (Thread-2): SQL status: BEGIN in 0.02 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.311048 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.311206 (Thread-2): On model.airbyte_utils.ticket_metrics_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_stg"} */
2022-04-19 13:15:46 normalization > drop view if exists "datalake"._airbyte_zendesk."ticket_metrics_stg__dbt_backup" cascade
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.320481 (Thread-1): On model.airbyte_utils.ticket_fields_stg: Close
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.337481 (Thread-1): 13:15:09 | 3 of 33 OK created view model _airbyte_zendesk.ticket_fields_stg............................................. [CREATE VIEW in 1.26s]
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.338368 (Thread-1): Finished running node model.airbyte_utils.ticket_fields_stg
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.338596 (Thread-1): Began running node model.airbyte_utils.brands
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.338855 (Thread-1): 13:15:09 | 6 of 33 START incremental model zendesk.brands............................................................... [RUN]
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.339810 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.brands".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.339990 (Thread-1): Compiling model.airbyte_utils.brands
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.352370 (Thread-2): SQL status: DROP VIEW in 0.02 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.353406 (Thread-2): On model.airbyte_utils.ticket_metrics_stg: COMMIT
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.353556 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.353680 (Thread-2): On model.airbyte_utils.ticket_metrics_stg: COMMIT
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.369835 (Thread-4): Using redshift connection "model.airbyte_utils.tags".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.370454 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.385928 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >   create temporary table
2022-04-19 13:15:46 normalization >     "sla_policies__dbt_tmp131508914710"
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >       compound sortkey(_airbyte_emitted_at)
2022-04-19 13:15:46 normalization >   as (
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization > with __dbt__cte__sla_policies_ab1 as (
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:46 normalization > -- depends_on: "datalake".zendesk._airbyte_raw_sla_policies
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'id', true) != '' then json_extract_path_text(_airbyte_data, 'id', true) end as id,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'url', true) != '' then json_extract_path_text(_airbyte_data, 'url', true) end as url,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'title', true) != '' then json_extract_path_text(_airbyte_data, 'title', true) end as title,
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >         case when json_extract_path_text(table_alias._airbyte_data, 'filter', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'filter', true) end
2022-04-19 13:15:46 normalization >      as filter,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'position', true) != '' then json_extract_path_text(_airbyte_data, 'position', true) end as position,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'created_at', true) != '' then json_extract_path_text(_airbyte_data, 'created_at', true) end as created_at,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'updated_at', true) != '' then json_extract_path_text(_airbyte_data, 'updated_at', true) end as updated_at,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'description', true) != '' then json_extract_path_text(_airbyte_data, 'description', true) end as description,
2022-04-19 13:15:46 normalization >     json_extract_path_text(_airbyte_data, 'policy_metrics', true) as policy_metrics,
2022-04-19 13:15:46 normalization >     _airbyte_ab_id,
2022-04-19 13:15:46 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:46 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:46 normalization > from "datalake".zendesk._airbyte_raw_sla_policies as table_alias
2022-04-19 13:15:46 normalization > -- sla_policies
2022-04-19 13:15:46 normalization > where 1 = 1
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > ),  __dbt__cte__sla_policies_ab2 as (
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:46 normalization > -- depends_on: __dbt__cte__sla_policies_ab1
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >     cast(id as 
2022-04-19 13:15:46 normalization >     bigint
2022-04-19 13:15:46 normalization > ) as id,
2022-04-19 13:15:46 normalization >     cast(url as varchar) as url,
2022-04-19 13:15:46 normalization >     cast(title as varchar) as title,
2022-04-19 13:15:46 normalization >     cast(filter as varchar) as filter,
2022-04-19 13:15:46 normalization >     cast(position as 
2022-04-19 13:15:46 normalization >     bigint
2022-04-19 13:15:46 normalization > ) as position,
2022-04-19 13:15:46 normalization >     cast(nullif(created_at, '') as 
2022-04-19 13:15:46 normalization >     timestamp with time zone
2022-04-19 13:15:46 normalization > ) as created_at,
2022-04-19 13:15:46 normalization >     cast(nullif(updated_at, '') as 
2022-04-19 13:15:46 normalization >     timestamp with time zone
2022-04-19 13:15:46 normalization > ) as updated_at,
2022-04-19 13:15:46 normalization >     cast(description as varchar) as description,
2022-04-19 13:15:46 normalization >     policy_metrics,
2022-04-19 13:15:46 normalization >     _airbyte_ab_id,
2022-04-19 13:15:46 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:46 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:46 normalization > from __dbt__cte__sla_policies_ab1
2022-04-19 13:15:46 normalization > -- sla_policies
2022-04-19 13:15:46 normalization > where 1 = 1
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > ),  __dbt__cte__sla_policies_ab3 as (
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > -- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:46 normalization > -- depends_on: __dbt__cte__sla_policies_ab2
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >     md5(cast(coalesce(cast(id as varchar), '') || '-' || coalesce(cast(url as varchar), '') || '-' || coalesce(cast(title as varchar), '') || '-' || coalesce(cast(filter as varchar), '') || '-' || coalesce(cast(position as varchar), '') || '-' || coalesce(cast(created_at as varchar), '') || '-' || coalesce(cast(updated_at as varchar), '') || '-' || coalesce(cast(description as varchar), '') || '-' || coalesce(cast(policy_metrics as varchar), '') as varchar)) as _airbyte_sla_policies_hashid,
2022-04-19 13:15:46 normalization >     tmp.*
2022-04-19 13:15:46 normalization > from __dbt__cte__sla_policies_ab2 tmp
2022-04-19 13:15:46 normalization > -- sla_policies
2022-04-19 13:15:46 normalization > where 1 = 1
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > )-- Final base SQL model
2022-04-19 13:15:46 normalization > -- depends_on: __dbt__cte__sla_policies_ab3
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >     id,
2022-04-19 13:15:46 normalization >     url,
2022-04-19 13:15:46 normalization >     title,
2022-04-19 13:15:46 normalization >     filter,
2022-04-19 13:15:46 normalization >     position,
2022-04-19 13:15:46 normalization >     created_at,
2022-04-19 13:15:46 normalization >     updated_at,
2022-04-19 13:15:46 normalization >     description,
2022-04-19 13:15:46 normalization >     policy_metrics,
2022-04-19 13:15:46 normalization >     _airbyte_ab_id,
2022-04-19 13:15:46 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:46 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:46 normalization >     _airbyte_sla_policies_hashid
2022-04-19 13:15:46 normalization > from __dbt__cte__sla_policies_ab3
2022-04-19 13:15:46 normalization > -- sla_policies from "datalake".zendesk._airbyte_raw_sla_policies
2022-04-19 13:15:46 normalization > where 1 = 1
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > and coalesce(
2022-04-19 13:15:46 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:46 normalization >     timestamp with time zone
2022-04-19 13:15:46 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:46 normalization >     timestamp with time zone
2022-04-19 13:15:46 normalization > )) from "datalake".zendesk."sla_policies"),
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >     true)
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >   );
2022-04-19 13:15:46 normalization >   
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.385771 (Thread-4): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >   create temporary table
2022-04-19 13:15:46 normalization >     "tags__dbt_tmp131508900954"
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >       compound sortkey(_airbyte_emitted_at)
2022-04-19 13:15:46 normalization >   as (
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization > with __dbt__cte__tags_ab1 as (
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:46 normalization > -- depends_on: "datalake".zendesk._airbyte_raw_tags
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'name', true) != '' then json_extract_path_text(_airbyte_data, 'name', true) end as name,
2022-04-19 13:15:46 normalization >     case when json_extract_path_text(_airbyte_data, 'count', true) != '' then json_extract_path_text(_airbyte_data, 'count', true) end as count,
2022-04-19 13:15:46 normalization >     _airbyte_ab_id,
2022-04-19 13:15:46 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:46 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:46 normalization > from "datalake".zendesk._airbyte_raw_tags as table_alias
2022-04-19 13:15:46 normalization > -- tags
2022-04-19 13:15:46 normalization > where 1 = 1
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > ),  __dbt__cte__tags_ab2 as (
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:46 normalization > -- depends_on: __dbt__cte__tags_ab1
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >     cast(name as varchar) as name,
2022-04-19 13:15:46 normalization >     cast(count as 
2022-04-19 13:15:46 normalization >     bigint
2022-04-19 13:15:46 normalization > ) as count,
2022-04-19 13:15:46 normalization >     _airbyte_ab_id,
2022-04-19 13:15:46 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:46 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:46 normalization > from __dbt__cte__tags_ab1
2022-04-19 13:15:46 normalization > -- tags
2022-04-19 13:15:46 normalization > where 1 = 1
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > ),  __dbt__cte__tags_ab3 as (
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > -- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:46 normalization > -- depends_on: __dbt__cte__tags_ab2
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >     md5(cast(coalesce(cast(name as varchar), '') || '-' || coalesce(cast(count as varchar), '') as varchar)) as _airbyte_tags_hashid,
2022-04-19 13:15:46 normalization >     tmp.*
2022-04-19 13:15:46 normalization > from __dbt__cte__tags_ab2 tmp
2022-04-19 13:15:46 normalization > -- tags
2022-04-19 13:15:46 normalization > where 1 = 1
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > )-- Final base SQL model
2022-04-19 13:15:46 normalization > -- depends_on: __dbt__cte__tags_ab3
2022-04-19 13:15:46 normalization > select
2022-04-19 13:15:46 normalization >     name,
2022-04-19 13:15:46 normalization >     count,
2022-04-19 13:15:46 normalization >     _airbyte_ab_id,
2022-04-19 13:15:46 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:46 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:46 normalization >     _airbyte_tags_hashid
2022-04-19 13:15:46 normalization > from __dbt__cte__tags_ab3
2022-04-19 13:15:46 normalization > -- tags from "datalake".zendesk._airbyte_raw_tags
2022-04-19 13:15:46 normalization > where 1 = 1
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization > and coalesce(
2022-04-19 13:15:46 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:46 normalization >     timestamp with time zone
2022-04-19 13:15:46 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:46 normalization >     timestamp with time zone
2022-04-19 13:15:46 normalization > )) from "datalake".zendesk."tags"),
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >     true)
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >   );
2022-04-19 13:15:46 normalization >   
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.385547 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.386301 (Thread-1): On model.airbyte_utils.brands: BEGIN
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.386444 (Thread-1): Opening a new connection, currently in state closed
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.386558 (Thread-1): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.432491 (Thread-2): SQL status: COMMIT in 0.08 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.432813 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.432950 (Thread-2): On model.airbyte_utils.ticket_metrics_stg: BEGIN
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.436840 (Thread-2): SQL status: BEGIN in 0.00 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.437571 (Thread-2): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.437773 (Thread-2): On model.airbyte_utils.ticket_metrics_stg: ROLLBACK
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.439751 (Thread-1): SQL status: BEGIN in 0.05 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.439919 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.440045 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >       with bound_views as (
2022-04-19 13:15:46 normalization >         select
2022-04-19 13:15:46 normalization >           ordinal_position,
2022-04-19 13:15:46 normalization >           table_schema,
2022-04-19 13:15:46 normalization >           column_name,
2022-04-19 13:15:46 normalization >           data_type,
2022-04-19 13:15:46 normalization >           character_maximum_length,
2022-04-19 13:15:46 normalization >           numeric_precision,
2022-04-19 13:15:46 normalization >           numeric_scale
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >         from information_schema."columns"
2022-04-19 13:15:46 normalization >         where table_name = 'brands'
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     unbound_views as (
2022-04-19 13:15:46 normalization >       select
2022-04-19 13:15:46 normalization >         ordinal_position,
2022-04-19 13:15:46 normalization >         view_schema,
2022-04-19 13:15:46 normalization >         col_name,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:46 normalization >             'character varying'
2022-04-19 13:15:46 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:46 normalization >           else col_type
2022-04-19 13:15:46 normalization >         end as col_type,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type like 'character%'
2022-04-19 13:15:46 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as character_maximum_length,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_precision,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_scale
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:46 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:46 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:46 normalization >       where view_name = 'brands'
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     external_views as (
2022-04-19 13:15:46 normalization >       select
2022-04-19 13:15:46 normalization >         columnnum,
2022-04-19 13:15:46 normalization >         schemaname,
2022-04-19 13:15:46 normalization >         columnname,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:46 normalization >           then 'character varying'
2022-04-19 13:15:46 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:46 normalization >           else external_type
2022-04-19 13:15:46 normalization >         end as external_type,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as character_maximum_length,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_precision,
2022-04-19 13:15:46 normalization >         case
2022-04-19 13:15:46 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:46 normalization >           then nullif(
2022-04-19 13:15:46 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:46 normalization >             '')::int
2022-04-19 13:15:46 normalization >           else null
2022-04-19 13:15:46 normalization >         end as numeric_scale
2022-04-19 13:15:46 normalization >       from
2022-04-19 13:15:46 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:46 normalization >       where
2022-04-19 13:15:46 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:46 normalization >         and tablename = 'brands'
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     ),
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     unioned as (
2022-04-19 13:15:46 normalization >       select * from bound_views
2022-04-19 13:15:46 normalization >       union all
2022-04-19 13:15:46 normalization >       select * from unbound_views
2022-04-19 13:15:46 normalization >       union all
2022-04-19 13:15:46 normalization >       select * from external_views
2022-04-19 13:15:46 normalization >     )
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     select
2022-04-19 13:15:46 normalization >       column_name,
2022-04-19 13:15:46 normalization >       data_type,
2022-04-19 13:15:46 normalization >       character_maximum_length,
2022-04-19 13:15:46 normalization >       numeric_precision,
2022-04-19 13:15:46 normalization >       numeric_scale
2022-04-19 13:15:46 normalization > 
2022-04-19 13:15:46 normalization >     from unioned
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:46 normalization >     
2022-04-19 13:15:46 normalization >     order by ordinal_position
2022-04-19 13:15:46 normalization >   
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.441305 (Thread-2): On model.airbyte_utils.ticket_metrics_stg: Close
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.442122 (Thread-2): 13:15:09 | 5 of 33 OK created view model _airbyte_zendesk.ticket_metrics_stg............................................ [CREATE VIEW in 1.14s]
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.442876 (Thread-2): Finished running node model.airbyte_utils.ticket_metrics_stg
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.443142 (Thread-2): Began running node model.airbyte_utils.tickets_stg
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.443694 (Thread-2): 13:15:09 | 7 of 33 START view model _airbyte_zendesk.tickets_stg........................................................ [RUN]
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.444377 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.tickets_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.444564 (Thread-2): Compiling model.airbyte_utils.tickets_stg
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.698517 (Thread-2): Writing injected SQL for node "model.airbyte_utils.tickets_stg"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.698990 (Thread-2): finished collecting timing info
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.703459 (Thread-2): Writing runtime SQL for node "model.airbyte_utils.tickets_stg"
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.703822 (Thread-2): Using redshift connection "model.airbyte_utils.tickets_stg".
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.703959 (Thread-2): On model.airbyte_utils.tickets_stg: BEGIN
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.704094 (Thread-2): Opening a new connection, currently in state closed
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.704207 (Thread-2): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.710830 (Thread-4): SQL status: SELECT in 0.32 seconds
2022-04-19 13:15:46 normalization > 2022-04-19 13:15:09.719008 (Thread-4): Using redshift connection "model.airbyte_utils.tags".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.719174 (Thread-4): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'tags__dbt_tmp131508900954'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'tags__dbt_tmp131508900954'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'None'
2022-04-19 13:15:47 normalization >         and tablename = 'tags__dbt_tmp131508900954'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.728876 (Thread-3): SQL status: SELECT in 0.34 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.733332 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.733496 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'sla_policies__dbt_tmp131508914710'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'sla_policies__dbt_tmp131508914710'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'None'
2022-04-19 13:15:47 normalization >         and tablename = 'sla_policies__dbt_tmp131508914710'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.740395 (Thread-1): SQL status: SELECT in 0.30 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.756820 (Thread-1): Writing injected SQL for node "model.airbyte_utils.brands"
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.757230 (Thread-1): finished collecting timing info
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.782475 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.782792 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'brands'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'brands'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'brands'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.789186 (Thread-2): SQL status: BEGIN in 0.09 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.789439 (Thread-2): Using redshift connection "model.airbyte_utils.tickets_stg".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.789573 (Thread-2): On model.airbyte_utils.tickets_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_stg"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >   create view "datalake"._airbyte_zendesk."tickets_stg__dbt_tmp" as (
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization > with __dbt__cte__tickets_ab1 as (
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:47 normalization > -- depends_on: "datalake".zendesk._airbyte_raw_tickets
2022-04-19 13:15:47 normalization > select
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'id', true) != '' then json_extract_path_text(_airbyte_data, 'id', true) end as id,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'url', true) != '' then json_extract_path_text(_airbyte_data, 'url', true) end as url,
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >         case when json_extract_path_text(table_alias._airbyte_data, 'via', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'via', true) end
2022-04-19 13:15:47 normalization >      as via,
2022-04-19 13:15:47 normalization >     json_extract_path_text(_airbyte_data, 'tags', true) as tags,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'type', true) != '' then json_extract_path_text(_airbyte_data, 'type', true) end as type,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'due_at', true) != '' then json_extract_path_text(_airbyte_data, 'due_at', true) end as due_at,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'status', true) != '' then json_extract_path_text(_airbyte_data, 'status', true) end as status,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'subject', true) != '' then json_extract_path_text(_airbyte_data, 'subject', true) end as subject,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'brand_id', true) != '' then json_extract_path_text(_airbyte_data, 'brand_id', true) end as brand_id,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'group_id', true) != '' then json_extract_path_text(_airbyte_data, 'group_id', true) end as group_id,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'priority', true) != '' then json_extract_path_text(_airbyte_data, 'priority', true) end as priority,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'is_public', true) != '' then json_extract_path_text(_airbyte_data, 'is_public', true) end as is_public,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'recipient', true) != '' then json_extract_path_text(_airbyte_data, 'recipient', true) end as recipient,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'created_at', true) != '' then json_extract_path_text(_airbyte_data, 'created_at', true) end as created_at,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'problem_id', true) != '' then json_extract_path_text(_airbyte_data, 'problem_id', true) end as problem_id,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'updated_at', true) != '' then json_extract_path_text(_airbyte_data, 'updated_at', true) end as updated_at,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'assignee_id', true) != '' then json_extract_path_text(_airbyte_data, 'assignee_id', true) end as assignee_id,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'description', true) != '' then json_extract_path_text(_airbyte_data, 'description', true) end as description,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'external_id', true) != '' then json_extract_path_text(_airbyte_data, 'external_id', true) end as external_id,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'raw_subject', true) != '' then json_extract_path_text(_airbyte_data, 'raw_subject', true) end as raw_subject,
2022-04-19 13:15:47 normalization >     json_extract_path_text(_airbyte_data, 'email_cc_ids', true) as email_cc_ids,
2022-04-19 13:15:47 normalization >     json_extract_path_text(_airbyte_data, 'follower_ids', true) as follower_ids,
2022-04-19 13:15:47 normalization >     json_extract_path_text(_airbyte_data, 'followup_ids', true) as followup_ids,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'requester_id', true) != '' then json_extract_path_text(_airbyte_data, 'requester_id', true) end as requester_id,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'submitter_id', true) != '' then json_extract_path_text(_airbyte_data, 'submitter_id', true) end as submitter_id,
2022-04-19 13:15:47 normalization >     json_extract_path_text(_airbyte_data, 'custom_fields', true) as custom_fields,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'has_incidents', true) != '' then json_extract_path_text(_airbyte_data, 'has_incidents', true) end as has_incidents,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'forum_topic_id', true) != '' then json_extract_path_text(_airbyte_data, 'forum_topic_id', true) end as forum_topic_id,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'ticket_form_id', true) != '' then json_extract_path_text(_airbyte_data, 'ticket_form_id', true) end as ticket_form_id,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'organization_id', true) != '' then json_extract_path_text(_airbyte_data, 'organization_id', true) end as organization_id,
2022-04-19 13:15:47 normalization >     json_extract_path_text(_airbyte_data, 'collaborator_ids', true) as collaborator_ids,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'allow_attachments', true) != '' then json_extract_path_text(_airbyte_data, 'allow_attachments', true) end as allow_attachments,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'allow_channelback', true) != '' then json_extract_path_text(_airbyte_data, 'allow_channelback', true) end as allow_channelback,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'generated_timestamp', true) != '' then json_extract_path_text(_airbyte_data, 'generated_timestamp', true) end as generated_timestamp,
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >         case when json_extract_path_text(table_alias._airbyte_data, 'satisfaction_rating', true) != '' then json_extract_path_text(table_alias._airbyte_data, 'satisfaction_rating', true) end
2022-04-19 13:15:47 normalization >      as satisfaction_rating,
2022-04-19 13:15:47 normalization >     json_extract_path_text(_airbyte_data, 'sharing_agreement_ids', true) as sharing_agreement_ids,
2022-04-19 13:15:47 normalization >     _airbyte_ab_id,
2022-04-19 13:15:47 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:47 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:47 normalization > from "datalake".zendesk._airbyte_raw_tickets as table_alias
2022-04-19 13:15:47 normalization > -- tickets
2022-04-19 13:15:47 normalization > where 1 = 1
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization > ),  __dbt__cte__tickets_ab2 as (
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:47 normalization > -- depends_on: __dbt__cte__tickets_ab1
2022-04-19 13:15:47 normalization > select
2022-04-19 13:15:47 normalization >     cast(id as 
2022-04-19 13:15:47 normalization >     bigint
2022-04-19 13:15:47 normalization > ) as id,
2022-04-19 13:15:47 normalization >     cast(url as varchar) as url,
2022-04-19 13:15:47 normalization >     cast(via as varchar) as via,
2022-04-19 13:15:47 normalization >     tags,
2022-04-19 13:15:47 normalization >     cast(type as varchar) as type,
2022-04-19 13:15:47 normalization >     cast(nullif(due_at, '') as 
2022-04-19 13:15:47 normalization >     timestamp with time zone
2022-04-19 13:15:47 normalization > ) as due_at,
2022-04-19 13:15:47 normalization >     cast(status as varchar) as status,
2022-04-19 13:15:47 normalization >     cast(subject as varchar) as subject,
2022-04-19 13:15:47 normalization >     cast(brand_id as 
2022-04-19 13:15:47 normalization >     bigint
2022-04-19 13:15:47 normalization > ) as brand_id,
2022-04-19 13:15:47 normalization >     cast(group_id as 
2022-04-19 13:15:47 normalization >     bigint
2022-04-19 13:15:47 normalization > ) as group_id,
2022-04-19 13:15:47 normalization >     cast(priority as varchar) as priority,
2022-04-19 13:15:47 normalization >     cast(decode(is_public, 'true', '1', 'false', '0')::integer as boolean) as is_public,
2022-04-19 13:15:47 normalization >     cast(recipient as varchar) as recipient,
2022-04-19 13:15:47 normalization >     cast(nullif(created_at, '') as 
2022-04-19 13:15:47 normalization >     timestamp with time zone
2022-04-19 13:15:47 normalization > ) as created_at,
2022-04-19 13:15:47 normalization >     cast(problem_id as 
2022-04-19 13:15:47 normalization >     bigint
2022-04-19 13:15:47 normalization > ) as problem_id,
2022-04-19 13:15:47 normalization >     cast(nullif(updated_at, '') as 
2022-04-19 13:15:47 normalization >     timestamp with time zone
2022-04-19 13:15:47 normalization > ) as updated_at,
2022-04-19 13:15:47 normalization >     cast(assignee_id as 
2022-04-19 13:15:47 normalization >     bigint
2022-04-19 13:15:47 normalization > ) as assignee_id,
2022-04-19 13:15:47 normalization >     cast(description as varchar) as description,
2022-04-19 13:15:47 normalization >     cast(external_id as varchar) as external_id,
2022-04-19 13:15:47 normalization >     cast(raw_subject as varchar) as raw_subject,
2022-04-19 13:15:47 normalization >     email_cc_ids,
2022-04-19 13:15:47 normalization >     follower_ids,
2022-04-19 13:15:47 normalization >     followup_ids,
2022-04-19 13:15:47 normalization >     cast(requester_id as 
2022-04-19 13:15:47 normalization >     bigint
2022-04-19 13:15:47 normalization > ) as requester_id,
2022-04-19 13:15:47 normalization >     cast(submitter_id as 
2022-04-19 13:15:47 normalization >     bigint
2022-04-19 13:15:47 normalization > ) as submitter_id,
2022-04-19 13:15:47 normalization >     custom_fields,
2022-04-19 13:15:47 normalization >     cast(decode(has_incidents, 'true', '1', 'false', '0')::integer as boolean) as has_incidents,
2022-04-19 13:15:47 normalization >     cast(forum_topic_id as 
2022-04-19 13:15:47 normalization >     bigint
2022-04-19 13:15:47 normalization > ) as forum_topic_id,
2022-04-19 13:15:47 normalization >     cast(ticket_form_id as 
2022-04-19 13:15:47 normalization >     bigint
2022-04-19 13:15:47 normalization > ) as ticket_form_id,
2022-04-19 13:15:47 normalization >     cast(organization_id as 
2022-04-19 13:15:47 normalization >     bigint
2022-04-19 13:15:47 normalization > ) as organization_id,
2022-04-19 13:15:47 normalization >     collaborator_ids,
2022-04-19 13:15:47 normalization >     cast(decode(allow_attachments, 'true', '1', 'false', '0')::integer as boolean) as allow_attachments,
2022-04-19 13:15:47 normalization >     cast(decode(allow_channelback, 'true', '1', 'false', '0')::integer as boolean) as allow_channelback,
2022-04-19 13:15:47 normalization >     cast(generated_timestamp as 
2022-04-19 13:15:47 normalization >     bigint
2022-04-19 13:15:47 normalization > ) as generated_timestamp,
2022-04-19 13:15:47 normalization >     cast(satisfaction_rating as varchar) as satisfaction_rating,
2022-04-19 13:15:47 normalization >     sharing_agreement_ids,
2022-04-19 13:15:47 normalization >     _airbyte_ab_id,
2022-04-19 13:15:47 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:47 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:47 normalization > from __dbt__cte__tickets_ab1
2022-04-19 13:15:47 normalization > -- tickets
2022-04-19 13:15:47 normalization > where 1 = 1
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization > )-- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:47 normalization > -- depends_on: __dbt__cte__tickets_ab2
2022-04-19 13:15:47 normalization > select
2022-04-19 13:15:47 normalization >     md5(cast(coalesce(cast(id as varchar), '') || '-' || coalesce(cast(url as varchar), '') || '-' || coalesce(cast(via as varchar), '') || '-' || coalesce(cast(tags as varchar), '') || '-' || coalesce(cast(type as varchar), '') || '-' || coalesce(cast(due_at as varchar), '') || '-' || coalesce(cast(status as varchar), '') || '-' || coalesce(cast(subject as varchar), '') || '-' || coalesce(cast(brand_id as varchar), '') || '-' || coalesce(cast(group_id as varchar), '') || '-' || coalesce(cast(priority as varchar), '') || '-' || coalesce(cast(case when is_public then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(recipient as varchar), '') || '-' || coalesce(cast(created_at as varchar), '') || '-' || coalesce(cast(problem_id as varchar), '') || '-' || coalesce(cast(updated_at as varchar), '') || '-' || coalesce(cast(assignee_id as varchar), '') || '-' || coalesce(cast(description as varchar), '') || '-' || coalesce(cast(external_id as varchar), '') || '-' || coalesce(cast(raw_subject as varchar), '') || '-' || coalesce(cast(email_cc_ids as varchar), '') || '-' || coalesce(cast(follower_ids as varchar), '') || '-' || coalesce(cast(followup_ids as varchar), '') || '-' || coalesce(cast(requester_id as varchar), '') || '-' || coalesce(cast(submitter_id as varchar), '') || '-' || coalesce(cast(custom_fields as varchar), '') || '-' || coalesce(cast(case when has_incidents then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(forum_topic_id as varchar), '') || '-' || coalesce(cast(ticket_form_id as varchar), '') || '-' || coalesce(cast(organization_id as varchar), '') || '-' || coalesce(cast(collaborator_ids as varchar), '') || '-' || coalesce(cast(case when allow_attachments then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(case when allow_channelback then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(generated_timestamp as varchar), '') || '-' || coalesce(cast(satisfaction_rating as varchar), '') || '-' || coalesce(cast(sharing_agreement_ids as varchar), '') as varchar)) as _airbyte_tickets_hashid,
2022-04-19 13:15:47 normalization >     tmp.*
2022-04-19 13:15:47 normalization > from __dbt__cte__tickets_ab2 tmp
2022-04-19 13:15:47 normalization > -- tickets
2022-04-19 13:15:47 normalization > where 1 = 1
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >   ) ;
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.829247 (Thread-2): SQL status: CREATE VIEW in 0.04 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.833655 (Thread-2): Using redshift connection "model.airbyte_utils.tickets_stg".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.833824 (Thread-2): On model.airbyte_utils.tickets_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_stg"} */
2022-04-19 13:15:47 normalization > alter table "datalake"."_airbyte_zendesk"."tickets_stg" rename to "tickets_stg__dbt_backup"
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.839904 (Thread-2): SQL status: ALTER TABLE in 0.01 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.849578 (Thread-2): Using redshift connection "model.airbyte_utils.tickets_stg".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.849771 (Thread-2): On model.airbyte_utils.tickets_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_stg"} */
2022-04-19 13:15:47 normalization > alter table "datalake"._airbyte_zendesk."tickets_stg__dbt_tmp" rename to "tickets_stg"
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.855119 (Thread-2): SQL status: ALTER TABLE in 0.01 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.857421 (Thread-2): On model.airbyte_utils.tickets_stg: COMMIT
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.857579 (Thread-2): Using redshift connection "model.airbyte_utils.tickets_stg".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.857700 (Thread-2): On model.airbyte_utils.tickets_stg: COMMIT
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.944560 (Thread-2): SQL status: COMMIT in 0.09 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.945153 (Thread-2): Using redshift connection "model.airbyte_utils.tickets_stg".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.945304 (Thread-2): On model.airbyte_utils.tickets_stg: BEGIN
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.949321 (Thread-2): SQL status: BEGIN in 0.00 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.952536 (Thread-2): Using redshift connection "model.airbyte_utils.tickets_stg".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.952691 (Thread-2): On model.airbyte_utils.tickets_stg: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_stg"} */
2022-04-19 13:15:47 normalization > drop view if exists "datalake"._airbyte_zendesk."tickets_stg__dbt_backup" cascade
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.971709 (Thread-2): SQL status: DROP VIEW in 0.02 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.972935 (Thread-2): On model.airbyte_utils.tickets_stg: COMMIT
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.973091 (Thread-2): Using redshift connection "model.airbyte_utils.tickets_stg".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:09.973218 (Thread-2): On model.airbyte_utils.tickets_stg: COMMIT
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.047336 (Thread-2): SQL status: COMMIT in 0.07 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.047667 (Thread-2): Using redshift connection "model.airbyte_utils.tickets_stg".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.047804 (Thread-2): On model.airbyte_utils.tickets_stg: BEGIN
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.051797 (Thread-2): SQL status: BEGIN in 0.00 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.052564 (Thread-2): finished collecting timing info
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.052779 (Thread-2): On model.airbyte_utils.tickets_stg: ROLLBACK
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.055990 (Thread-2): On model.airbyte_utils.tickets_stg: Close
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.056877 (Thread-2): 13:15:10 | 7 of 33 OK created view model _airbyte_zendesk.tickets_stg................................................... [CREATE VIEW in 0.61s]
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.057294 (Thread-2): Finished running node model.airbyte_utils.tickets_stg
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.058054 (Thread-2): Began running node model.airbyte_utils.ticket_metric_events_scd
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.058615 (Thread-2): 13:15:10 | 8 of 33 START incremental model zendesk.ticket_metric_events_scd............................................. [RUN]
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.059193 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.059373 (Thread-2): Compiling model.airbyte_utils.ticket_metric_events_scd
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.101775 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.102031 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: BEGIN
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.102175 (Thread-2): Opening a new connection, currently in state closed
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.102287 (Thread-2): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.125741 (Thread-1): SQL status: SELECT in 0.34 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.131823 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.131998 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >   create temporary table
2022-04-19 13:15:47 normalization >     "brands__dbt_tmp131509771308"
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >       compound sortkey(_airbyte_emitted_at)
2022-04-19 13:15:47 normalization >   as (
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization > with __dbt__cte__brands_ab1 as (
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:47 normalization > -- depends_on: "datalake".zendesk._airbyte_raw_brands
2022-04-19 13:15:47 normalization > select
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'id', true) != '' then json_extract_path_text(_airbyte_data, 'id', true) end as id,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'url', true) != '' then json_extract_path_text(_airbyte_data, 'url', true) end as url,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'logo', true) != '' then json_extract_path_text(_airbyte_data, 'logo', true) end as logo,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'name', true) != '' then json_extract_path_text(_airbyte_data, 'name', true) end as name,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'active', true) != '' then json_extract_path_text(_airbyte_data, 'active', true) end as active,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'default', true) != '' then json_extract_path_text(_airbyte_data, 'default', true) end as "default",
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'brand_url', true) != '' then json_extract_path_text(_airbyte_data, 'brand_url', true) end as brand_url,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'subdomain', true) != '' then json_extract_path_text(_airbyte_data, 'subdomain', true) end as subdomain,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'created_at', true) != '' then json_extract_path_text(_airbyte_data, 'created_at', true) end as created_at,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'is_deleted', true) != '' then json_extract_path_text(_airbyte_data, 'is_deleted', true) end as is_deleted,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'updated_at', true) != '' then json_extract_path_text(_airbyte_data, 'updated_at', true) end as updated_at,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'host_mapping', true) != '' then json_extract_path_text(_airbyte_data, 'host_mapping', true) end as host_mapping,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'has_help_center', true) != '' then json_extract_path_text(_airbyte_data, 'has_help_center', true) end as has_help_center,
2022-04-19 13:15:47 normalization >     json_extract_path_text(_airbyte_data, 'ticket_form_ids', true) as ticket_form_ids,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'help_center_state', true) != '' then json_extract_path_text(_airbyte_data, 'help_center_state', true) end as help_center_state,
2022-04-19 13:15:47 normalization >     case when json_extract_path_text(_airbyte_data, 'signature_template', true) != '' then json_extract_path_text(_airbyte_data, 'signature_template', true) end as signature_template,
2022-04-19 13:15:47 normalization >     _airbyte_ab_id,
2022-04-19 13:15:47 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:47 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:47 normalization > from "datalake".zendesk._airbyte_raw_brands as table_alias
2022-04-19 13:15:47 normalization > -- brands
2022-04-19 13:15:47 normalization > where 1 = 1
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization > ),  __dbt__cte__brands_ab2 as (
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:47 normalization > -- depends_on: __dbt__cte__brands_ab1
2022-04-19 13:15:47 normalization > select
2022-04-19 13:15:47 normalization >     cast(id as 
2022-04-19 13:15:47 normalization >     bigint
2022-04-19 13:15:47 normalization > ) as id,
2022-04-19 13:15:47 normalization >     cast(url as varchar) as url,
2022-04-19 13:15:47 normalization >     cast(logo as varchar) as logo,
2022-04-19 13:15:47 normalization >     cast(name as varchar) as name,
2022-04-19 13:15:47 normalization >     cast(decode(active, 'true', '1', 'false', '0')::integer as boolean) as active,
2022-04-19 13:15:47 normalization >     cast(decode("default", 'true', '1', 'false', '0')::integer as boolean) as "default",
2022-04-19 13:15:47 normalization >     cast(brand_url as varchar) as brand_url,
2022-04-19 13:15:47 normalization >     cast(subdomain as varchar) as subdomain,
2022-04-19 13:15:47 normalization >     cast(nullif(created_at, '') as 
2022-04-19 13:15:47 normalization >     timestamp with time zone
2022-04-19 13:15:47 normalization > ) as created_at,
2022-04-19 13:15:47 normalization >     cast(decode(is_deleted, 'true', '1', 'false', '0')::integer as boolean) as is_deleted,
2022-04-19 13:15:47 normalization >     cast(nullif(updated_at, '') as 
2022-04-19 13:15:47 normalization >     timestamp with time zone
2022-04-19 13:15:47 normalization > ) as updated_at,
2022-04-19 13:15:47 normalization >     cast(host_mapping as varchar) as host_mapping,
2022-04-19 13:15:47 normalization >     cast(decode(has_help_center, 'true', '1', 'false', '0')::integer as boolean) as has_help_center,
2022-04-19 13:15:47 normalization >     ticket_form_ids,
2022-04-19 13:15:47 normalization >     cast(help_center_state as varchar) as help_center_state,
2022-04-19 13:15:47 normalization >     cast(signature_template as varchar) as signature_template,
2022-04-19 13:15:47 normalization >     _airbyte_ab_id,
2022-04-19 13:15:47 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:47 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:47 normalization > from __dbt__cte__brands_ab1
2022-04-19 13:15:47 normalization > -- brands
2022-04-19 13:15:47 normalization > where 1 = 1
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization > ),  __dbt__cte__brands_ab3 as (
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization > -- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:47 normalization > -- depends_on: __dbt__cte__brands_ab2
2022-04-19 13:15:47 normalization > select
2022-04-19 13:15:47 normalization >     md5(cast(coalesce(cast(id as varchar), '') || '-' || coalesce(cast(url as varchar), '') || '-' || coalesce(cast(logo as varchar), '') || '-' || coalesce(cast(name as varchar), '') || '-' || coalesce(cast(case when active then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(case when "default" then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(brand_url as varchar), '') || '-' || coalesce(cast(subdomain as varchar), '') || '-' || coalesce(cast(created_at as varchar), '') || '-' || coalesce(cast(case when is_deleted then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(updated_at as varchar), '') || '-' || coalesce(cast(host_mapping as varchar), '') || '-' || coalesce(cast(case when has_help_center then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(ticket_form_ids as varchar), '') || '-' || coalesce(cast(help_center_state as varchar), '') || '-' || coalesce(cast(signature_template as varchar), '') as varchar)) as _airbyte_brands_hashid,
2022-04-19 13:15:47 normalization >     tmp.*
2022-04-19 13:15:47 normalization > from __dbt__cte__brands_ab2 tmp
2022-04-19 13:15:47 normalization > -- brands
2022-04-19 13:15:47 normalization > where 1 = 1
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization > )-- Final base SQL model
2022-04-19 13:15:47 normalization > -- depends_on: __dbt__cte__brands_ab3
2022-04-19 13:15:47 normalization > select
2022-04-19 13:15:47 normalization >     id,
2022-04-19 13:15:47 normalization >     url,
2022-04-19 13:15:47 normalization >     logo,
2022-04-19 13:15:47 normalization >     name,
2022-04-19 13:15:47 normalization >     active,
2022-04-19 13:15:47 normalization >     "default",
2022-04-19 13:15:47 normalization >     brand_url,
2022-04-19 13:15:47 normalization >     subdomain,
2022-04-19 13:15:47 normalization >     created_at,
2022-04-19 13:15:47 normalization >     is_deleted,
2022-04-19 13:15:47 normalization >     updated_at,
2022-04-19 13:15:47 normalization >     host_mapping,
2022-04-19 13:15:47 normalization >     has_help_center,
2022-04-19 13:15:47 normalization >     ticket_form_ids,
2022-04-19 13:15:47 normalization >     help_center_state,
2022-04-19 13:15:47 normalization >     signature_template,
2022-04-19 13:15:47 normalization >     _airbyte_ab_id,
2022-04-19 13:15:47 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:47 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:47 normalization >     _airbyte_brands_hashid
2022-04-19 13:15:47 normalization > from __dbt__cte__brands_ab3
2022-04-19 13:15:47 normalization > -- brands from "datalake".zendesk._airbyte_raw_brands
2022-04-19 13:15:47 normalization > where 1 = 1
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization > and coalesce(
2022-04-19 13:15:47 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:47 normalization >     timestamp with time zone
2022-04-19 13:15:47 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:47 normalization >     timestamp with time zone
2022-04-19 13:15:47 normalization > )) from "datalake".zendesk."brands"),
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     true)
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >   );
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.144381 (Thread-4): SQL status: SELECT in 0.43 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.148992 (Thread-4): Using redshift connection "model.airbyte_utils.tags".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.149151 (Thread-4): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'tags'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'tags'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'tags'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.187864 (Thread-2): SQL status: BEGIN in 0.09 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.188142 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.188306 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.199723 (Thread-3): SQL status: SELECT in 0.47 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.204556 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.204722 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'sla_policies'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'sla_policies'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'sla_policies'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.553545 (Thread-1): SQL status: SELECT in 0.42 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.559366 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.559544 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'brands__dbt_tmp131509771308'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'brands__dbt_tmp131509771308'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'None'
2022-04-19 13:15:47 normalization >         and tablename = 'brands__dbt_tmp131509771308'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.585584 (Thread-4): SQL status: SELECT in 0.44 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.620455 (Thread-4): Using redshift connection "model.airbyte_utils.tags".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.620714 (Thread-4): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'tags__dbt_tmp131508900954'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'tags__dbt_tmp131508900954'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'None'
2022-04-19 13:15:47 normalization >         and tablename = 'tags__dbt_tmp131508900954'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.645883 (Thread-3): SQL status: SELECT in 0.44 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.651272 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.651450 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'sla_policies__dbt_tmp131508914710'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'sla_policies__dbt_tmp131508914710'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'None'
2022-04-19 13:15:47 normalization >         and tablename = 'sla_policies__dbt_tmp131508914710'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.749798 (Thread-2): SQL status: SELECT in 0.56 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.757244 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.757458 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.930823 (Thread-1): SQL status: SELECT in 0.37 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.936139 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:10.936357 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'brands'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'brands'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'brands'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.076375 (Thread-3): SQL status: SELECT in 0.42 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.081590 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.081773 (Thread-4): SQL status: SELECT in 0.46 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.088043 (Thread-4): Using redshift connection "model.airbyte_utils.tags".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.081955 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'sla_policies'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'sla_policies'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'sla_policies'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.088203 (Thread-4): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'tags'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'tags'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'tags'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.124704 (Thread-2): SQL status: SELECT in 0.37 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.191876 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.192126 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metric_events_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metric_events_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metric_events_stg'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.270212 (Thread-1): SQL status: SELECT in 0.33 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.275913 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.276093 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'brands__dbt_tmp131509771308'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'brands__dbt_tmp131509771308'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'None'
2022-04-19 13:15:47 normalization >         and tablename = 'brands__dbt_tmp131509771308'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.551237 (Thread-4): SQL status: SELECT in 0.46 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.576435 (Thread-3): SQL status: SELECT in 0.49 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.579986 (Thread-3): 
2022-04-19 13:15:47 normalization >     In "datalake"."zendesk"."sla_policies":
2022-04-19 13:15:47 normalization >         Schema changed: False
2022-04-19 13:15:47 normalization >         Source columns not in target: []
2022-04-19 13:15:47 normalization >         Target columns not in source: []
2022-04-19 13:15:47 normalization >         New column types: []
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.602175 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.571769 (Thread-4): 
2022-04-19 13:15:47 normalization >     In "datalake"."zendesk"."tags":
2022-04-19 13:15:47 normalization >         Schema changed: False
2022-04-19 13:15:47 normalization >         Source columns not in target: []
2022-04-19 13:15:47 normalization >         Target columns not in source: []
2022-04-19 13:15:47 normalization >         New column types: []
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.605519 (Thread-4): Using redshift connection "model.airbyte_utils.tags".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.605685 (Thread-4): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'tags'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'tags'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'tags'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.613918 (Thread-2): SQL status: SELECT in 0.41 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.615974 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.616155 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.620394 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.620567 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.620712 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.620865 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.621002 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.621135 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.621265 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.621394 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.602479 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'sla_policies'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'sla_policies'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'sla_policies'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.628394 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.628731 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.681076 (Thread-1): SQL status: SELECT in 0.40 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.687939 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:11.688138 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'brands'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'brands'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'brands'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.020356 (Thread-3): SQL status: SELECT in 0.39 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.022967 (Thread-3): Writing runtime SQL for node "model.airbyte_utils.sla_policies"
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.023437 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.023579 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       delete
2022-04-19 13:15:47 normalization >     from "datalake".zendesk."sla_policies"
2022-04-19 13:15:47 normalization >     where (_airbyte_ab_id) in (
2022-04-19 13:15:47 normalization >         select (_airbyte_ab_id)
2022-04-19 13:15:47 normalization >         from "sla_policies__dbt_tmp131508914710"
2022-04-19 13:15:47 normalization >     );
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     insert into "datalake".zendesk."sla_policies" ("id", "url", "title", "filter", "position", "created_at", "updated_at", "description", "policy_metrics", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_sla_policies_hashid")
2022-04-19 13:15:47 normalization >     (
2022-04-19 13:15:47 normalization >        select "id", "url", "title", "filter", "position", "created_at", "updated_at", "description", "policy_metrics", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_sla_policies_hashid"
2022-04-19 13:15:47 normalization >        from "sla_policies__dbt_tmp131508914710"
2022-04-19 13:15:47 normalization >     );
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.039794 (Thread-4): SQL status: SELECT in 0.43 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.041842 (Thread-4): Writing runtime SQL for node "model.airbyte_utils.tags"
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.042203 (Thread-4): Using redshift connection "model.airbyte_utils.tags".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.042341 (Thread-4): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       delete
2022-04-19 13:15:47 normalization >     from "datalake".zendesk."tags"
2022-04-19 13:15:47 normalization >     where (_airbyte_ab_id) in (
2022-04-19 13:15:47 normalization >         select (_airbyte_ab_id)
2022-04-19 13:15:47 normalization >         from "tags__dbt_tmp131508900954"
2022-04-19 13:15:47 normalization >     );
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     insert into "datalake".zendesk."tags" ("name", "count", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_tags_hashid")
2022-04-19 13:15:47 normalization >     (
2022-04-19 13:15:47 normalization >        select "name", "count", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_tags_hashid"
2022-04-19 13:15:47 normalization >        from "tags__dbt_tmp131508900954"
2022-04-19 13:15:47 normalization >     );
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.107312 (Thread-2): SQL status: SELECT in 0.48 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.109646 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.109821 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.109965 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.110098 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.110226 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.110350 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.110473 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.110596 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.110718 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.110840 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.110962 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.111084 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.111214 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.111336 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.111463 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.111617 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.111759 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.111895 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.112028 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.112161 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.112327 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.112470 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.112605 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.112741 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.112876 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.140414 (Thread-1): SQL status: SELECT in 0.45 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.153506 (Thread-1): 
2022-04-19 13:15:47 normalization >     In "datalake"."zendesk"."brands":
2022-04-19 13:15:47 normalization >         Schema changed: False
2022-04-19 13:15:47 normalization >         Source columns not in target: []
2022-04-19 13:15:47 normalization >         Target columns not in source: []
2022-04-19 13:15:47 normalization >         New column types: []
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.160404 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.160861 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'brands'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'brands'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'brands'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.160697 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.161137 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metric_events_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metric_events_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metric_events_stg'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.193302 (Thread-3): SQL status: INSERT 0 11 in 0.17 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.198176 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.198342 (Thread-3): On model.airbyte_utils.sla_policies: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'sla_policies'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'sla_policies'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'sla_policies'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.217898 (Thread-4): SQL status: INSERT 0 196 in 0.18 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.222774 (Thread-4): Using redshift connection "model.airbyte_utils.tags".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.222941 (Thread-4): On model.airbyte_utils.tags: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tags"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'tags'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'tags'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'tags'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.574037 (Thread-3): SQL status: SELECT in 0.38 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.576580 (Thread-3): On model.airbyte_utils.sla_policies: COMMIT
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.576740 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.576865 (Thread-3): On model.airbyte_utils.sla_policies: COMMIT
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.579510 (Thread-2): SQL status: SELECT in 0.42 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.581283 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.581460 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.581600 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.581740 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.581867 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.581993 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.582115 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.582236 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.582358 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.582480 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.582625 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.582764 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.582895 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.583026 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.583161 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.583292 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.583421 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.583843 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.583986 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.584120 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.591619 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.591783 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metric_events_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metric_events_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metric_events_stg'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.624365 (Thread-1): SQL status: SELECT in 0.46 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.627387 (Thread-1): Writing runtime SQL for node "model.airbyte_utils.brands"
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.627776 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.627914 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       delete
2022-04-19 13:15:47 normalization >     from "datalake".zendesk."brands"
2022-04-19 13:15:47 normalization >     where (_airbyte_ab_id) in (
2022-04-19 13:15:47 normalization >         select (_airbyte_ab_id)
2022-04-19 13:15:47 normalization >         from "brands__dbt_tmp131509771308"
2022-04-19 13:15:47 normalization >     );
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     insert into "datalake".zendesk."brands" ("id", "url", "logo", "name", "active", "default", "brand_url", "subdomain", "created_at", "is_deleted", "updated_at", "host_mapping", "has_help_center", "ticket_form_ids", "help_center_state", "signature_template", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_brands_hashid")
2022-04-19 13:15:47 normalization >     (
2022-04-19 13:15:47 normalization >        select "id", "url", "logo", "name", "active", "default", "brand_url", "subdomain", "created_at", "is_deleted", "updated_at", "host_mapping", "has_help_center", "ticket_form_ids", "help_center_state", "signature_template", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_brands_hashid"
2022-04-19 13:15:47 normalization >        from "brands__dbt_tmp131509771308"
2022-04-19 13:15:47 normalization >     );
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.631849 (Thread-4): SQL status: SELECT in 0.41 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.633787 (Thread-4): On model.airbyte_utils.tags: COMMIT
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.633945 (Thread-4): Using redshift connection "model.airbyte_utils.tags".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.634076 (Thread-4): On model.airbyte_utils.tags: COMMIT
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.773343 (Thread-3): SQL status: COMMIT in 0.20 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.774224 (Thread-3): finished collecting timing info
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.774438 (Thread-3): On model.airbyte_utils.sla_policies: Close
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.775282 (Thread-3): 13:15:12 | 4 of 33 OK created incremental model zendesk.sla_policies.................................................... [INSERT 0 11 in 4.71s]
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.775803 (Thread-4): SQL status: COMMIT in 0.14 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.776474 (Thread-4): finished collecting timing info
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.776662 (Thread-4): On model.airbyte_utils.tags: Close
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.777297 (Thread-4): 13:15:12 | 2 of 33 OK created incremental model zendesk.tags............................................................ [INSERT 0 196 in 5.10s]
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.777495 (Thread-3): Finished running node model.airbyte_utils.sla_policies
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.777794 (Thread-3): Began running node model.airbyte_utils.ticket_fields_scd
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.778951 (Thread-3): 13:15:12 | 9 of 33 START incremental model zendesk.ticket_fields_scd.................................................... [RUN]
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.778608 (Thread-4): Finished running node model.airbyte_utils.tags
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.779410 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_scd
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.779805 (Thread-4): 13:15:12 | 10 of 33 START incremental model zendesk.ticket_metrics_scd.................................................. [RUN]
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.780174 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.784390 (Thread-3): Compiling model.airbyte_utils.ticket_fields_scd
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.794238 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.794459 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_scd
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.827387 (Thread-2): SQL status: SELECT in 0.24 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.829517 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.829700 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.829853 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.830006 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.830149 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.830290 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.830431 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.830571 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.830710 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.830849 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.831017 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.831171 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.831325 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.831475 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.831623 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.831770 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.831918 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.832072 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.832218 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.834748 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.838737 (Thread-2): Writing injected SQL for node "model.airbyte_utils.ticket_metric_events_scd"
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.839184 (Thread-2): finished collecting timing info
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.851066 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.868441 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: BEGIN
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.876401 (Thread-4): Opening a new connection, currently in state closed
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.883708 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.884048 (Thread-3): On model.airbyte_utils.ticket_fields_scd: BEGIN
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.884189 (Thread-3): Opening a new connection, currently in state closed
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.896485 (Thread-1): SQL status: INSERT 0 12 in 0.27 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.906899 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.907102 (Thread-1): On model.airbyte_utils.brands: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.brands"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'brands'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'brands'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'brands'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.896699 (Thread-3): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.880248 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.907789 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.883874 (Thread-4): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.961591 (Thread-4): SQL status: BEGIN in 0.09 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.961889 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.962026 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metrics_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metrics_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metrics_scd'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.966453 (Thread-3): SQL status: BEGIN in 0.08 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.966637 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:12.966767 (Thread-3): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_fields_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_fields_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_fields_scd'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.331527 (Thread-1): SQL status: SELECT in 0.42 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.334486 (Thread-1): On model.airbyte_utils.brands: COMMIT
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.334656 (Thread-1): Using redshift connection "model.airbyte_utils.brands".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.334783 (Thread-1): On model.airbyte_utils.brands: COMMIT
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.377007 (Thread-2): SQL status: SELECT in 0.47 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.382738 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.382904 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >   create temporary table
2022-04-19 13:15:47 normalization >     "ticket_metric_events_scd__dbt_tmp131512877080"
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >       compound sortkey(_airbyte_active_row,_airbyte_unique_key_scd,_airbyte_emitted_at)
2022-04-19 13:15:47 normalization >   as (
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization > -- depends_on: ref('ticket_metric_events_stg')
2022-04-19 13:15:47 normalization > with
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization > new_data as (
2022-04-19 13:15:47 normalization >     -- retrieve incremental "new" data
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >         *
2022-04-19 13:15:47 normalization >     from "datalake"._airbyte_zendesk."ticket_metric_events_stg"
2022-04-19 13:15:47 normalization >     -- ticket_metric_events from "datalake".zendesk._airbyte_raw_ticket_metric_events
2022-04-19 13:15:47 normalization >     where 1 = 1
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization > and coalesce(
2022-04-19 13:15:47 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:47 normalization >     timestamp with time zone
2022-04-19 13:15:47 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:47 normalization >     timestamp with time zone
2022-04-19 13:15:47 normalization > )) from "datalake".zendesk."ticket_metric_events_scd"),
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     true)
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization > ),
2022-04-19 13:15:47 normalization > new_data_ids as (
2022-04-19 13:15:47 normalization >     -- build a subset of _airbyte_unique_key from rows that are new
2022-04-19 13:15:47 normalization >     select distinct
2022-04-19 13:15:47 normalization >         md5(cast(coalesce(cast(id as varchar), '') as varchar)) as _airbyte_unique_key
2022-04-19 13:15:47 normalization >     from new_data
2022-04-19 13:15:47 normalization > ),
2022-04-19 13:15:47 normalization > empty_new_data as (
2022-04-19 13:15:47 normalization >     -- build an empty table to only keep the table's column types
2022-04-19 13:15:47 normalization >     select * from new_data where 1 = 0
2022-04-19 13:15:47 normalization > ),
2022-04-19 13:15:47 normalization > previous_active_scd_data as (
2022-04-19 13:15:47 normalization >     -- retrieve "incomplete old" data that needs to be updated with an end date because of new changes
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >         this_data."_airbyte_ticket_metric_events_hashid",
2022-04-19 13:15:47 normalization >   this_data."id",
2022-04-19 13:15:47 normalization >   this_data."time",
2022-04-19 13:15:47 normalization >   this_data."type",
2022-04-19 13:15:47 normalization >   this_data."metric",
2022-04-19 13:15:47 normalization >   this_data."ticket_id",
2022-04-19 13:15:47 normalization >   this_data."instance_id",
2022-04-19 13:15:47 normalization >   this_data."_airbyte_ab_id",
2022-04-19 13:15:47 normalization >   this_data."_airbyte_emitted_at",
2022-04-19 13:15:47 normalization >   this_data."_airbyte_normalized_at"
2022-04-19 13:15:47 normalization >     from "datalake".zendesk."ticket_metric_events_scd" as this_data
2022-04-19 13:15:47 normalization >     -- make a join with new_data using primary key to filter active data that need to be updated only
2022-04-19 13:15:47 normalization >     join new_data_ids on this_data._airbyte_unique_key = new_data_ids._airbyte_unique_key
2022-04-19 13:15:47 normalization >     -- force left join to NULL values (we just need to transfer column types only for the star_intersect macro on schema changes)
2022-04-19 13:15:47 normalization >     left join empty_new_data as inc_data on this_data._airbyte_ab_id = inc_data._airbyte_ab_id
2022-04-19 13:15:47 normalization >     where _airbyte_active_row = 1
2022-04-19 13:15:47 normalization > ),
2022-04-19 13:15:47 normalization > input_data as (
2022-04-19 13:15:47 normalization >     select "_airbyte_ticket_metric_events_hashid",
2022-04-19 13:15:47 normalization >   "id",
2022-04-19 13:15:47 normalization >   "time",
2022-04-19 13:15:47 normalization >   "type",
2022-04-19 13:15:47 normalization >   "metric",
2022-04-19 13:15:47 normalization >   "ticket_id",
2022-04-19 13:15:47 normalization >   "instance_id",
2022-04-19 13:15:47 normalization >   "_airbyte_ab_id",
2022-04-19 13:15:47 normalization >   "_airbyte_emitted_at",
2022-04-19 13:15:47 normalization >   "_airbyte_normalized_at" from new_data
2022-04-19 13:15:47 normalization >     union all
2022-04-19 13:15:47 normalization >     select "_airbyte_ticket_metric_events_hashid",
2022-04-19 13:15:47 normalization >   "id",
2022-04-19 13:15:47 normalization >   "time",
2022-04-19 13:15:47 normalization >   "type",
2022-04-19 13:15:47 normalization >   "metric",
2022-04-19 13:15:47 normalization >   "ticket_id",
2022-04-19 13:15:47 normalization >   "instance_id",
2022-04-19 13:15:47 normalization >   "_airbyte_ab_id",
2022-04-19 13:15:47 normalization >   "_airbyte_emitted_at",
2022-04-19 13:15:47 normalization >   "_airbyte_normalized_at" from previous_active_scd_data
2022-04-19 13:15:47 normalization > ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization > scd_data as (
2022-04-19 13:15:47 normalization >     -- SQL model to build a Type 2 Slowly Changing Dimension (SCD) table for each record identified by their primary key
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       md5(cast(coalesce(cast(id as varchar), '') as varchar)) as _airbyte_unique_key,
2022-04-19 13:15:47 normalization >       id,
2022-04-19 13:15:47 normalization >       "time",
2022-04-19 13:15:47 normalization >       type,
2022-04-19 13:15:47 normalization >       metric,
2022-04-19 13:15:47 normalization >       ticket_id,
2022-04-19 13:15:47 normalization >       instance_id,
2022-04-19 13:15:47 normalization >       "time" as _airbyte_start_at,
2022-04-19 13:15:47 normalization >       lag("time") over (
2022-04-19 13:15:47 normalization >         partition by id
2022-04-19 13:15:47 normalization >         order by
2022-04-19 13:15:47 normalization >             "time" is null asc,
2022-04-19 13:15:47 normalization >             "time" desc,
2022-04-19 13:15:47 normalization >             _airbyte_emitted_at desc
2022-04-19 13:15:47 normalization >       ) as _airbyte_end_at,
2022-04-19 13:15:47 normalization >       case when row_number() over (
2022-04-19 13:15:47 normalization >         partition by id
2022-04-19 13:15:47 normalization >         order by
2022-04-19 13:15:47 normalization >             "time" is null asc,
2022-04-19 13:15:47 normalization >             "time" desc,
2022-04-19 13:15:47 normalization >             _airbyte_emitted_at desc
2022-04-19 13:15:47 normalization >       ) = 1 then 1 else 0 end as _airbyte_active_row,
2022-04-19 13:15:47 normalization >       _airbyte_ab_id,
2022-04-19 13:15:47 normalization >       _airbyte_emitted_at,
2022-04-19 13:15:47 normalization >       _airbyte_ticket_metric_events_hashid
2022-04-19 13:15:47 normalization >     from input_data
2022-04-19 13:15:47 normalization > ),
2022-04-19 13:15:47 normalization > dedup_data as (
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >         -- we need to ensure de-duplicated rows for merge/update queries
2022-04-19 13:15:47 normalization >         -- additionally, we generate a unique key for the scd table
2022-04-19 13:15:47 normalization >         row_number() over (
2022-04-19 13:15:47 normalization >             partition by
2022-04-19 13:15:47 normalization >                 _airbyte_unique_key,
2022-04-19 13:15:47 normalization >                 _airbyte_start_at,
2022-04-19 13:15:47 normalization >                 _airbyte_emitted_at
2022-04-19 13:15:47 normalization >             order by _airbyte_active_row desc, _airbyte_ab_id
2022-04-19 13:15:47 normalization >         ) as _airbyte_row_num,
2022-04-19 13:15:47 normalization >         md5(cast(coalesce(cast(_airbyte_unique_key as varchar), '') || '-' || coalesce(cast(_airbyte_start_at as varchar), '') || '-' || coalesce(cast(_airbyte_emitted_at as varchar), '') as varchar)) as _airbyte_unique_key_scd,
2022-04-19 13:15:47 normalization >         scd_data.*
2022-04-19 13:15:47 normalization >     from scd_data
2022-04-19 13:15:47 normalization > )
2022-04-19 13:15:47 normalization > select
2022-04-19 13:15:47 normalization >     _airbyte_unique_key,
2022-04-19 13:15:47 normalization >     _airbyte_unique_key_scd,
2022-04-19 13:15:47 normalization >     id,
2022-04-19 13:15:47 normalization >     "time",
2022-04-19 13:15:47 normalization >     type,
2022-04-19 13:15:47 normalization >     metric,
2022-04-19 13:15:47 normalization >     ticket_id,
2022-04-19 13:15:47 normalization >     instance_id,
2022-04-19 13:15:47 normalization >     _airbyte_start_at,
2022-04-19 13:15:47 normalization >     _airbyte_end_at,
2022-04-19 13:15:47 normalization >     _airbyte_active_row,
2022-04-19 13:15:47 normalization >     _airbyte_ab_id,
2022-04-19 13:15:47 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:47 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:47 normalization >     _airbyte_ticket_metric_events_hashid
2022-04-19 13:15:47 normalization > from dedup_data where _airbyte_row_num = 1
2022-04-19 13:15:47 normalization >   );
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.485640 (Thread-3): SQL status: SELECT in 0.52 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.493858 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.494049 (Thread-3): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_fields_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_fields_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_fields_scd'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.519258 (Thread-4): SQL status: SELECT in 0.56 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.525502 (Thread-1): SQL status: COMMIT in 0.19 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.526117 (Thread-1): finished collecting timing info
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.526325 (Thread-1): On model.airbyte_utils.brands: Close
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.527121 (Thread-1): 13:15:13 | 6 of 33 OK created incremental model zendesk.brands.......................................................... [INSERT 0 12 in 4.19s]
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.535481 (Thread-1): Finished running node model.airbyte_utils.brands
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.535747 (Thread-1): Began running node model.airbyte_utils.tickets_scd
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.536210 (Thread-1): 13:15:13 | 11 of 33 START incremental model zendesk.tickets_scd......................................................... [RUN]
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.536985 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.tickets_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.537178 (Thread-1): Compiling model.airbyte_utils.tickets_scd
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.554870 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.555088 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metrics_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metrics_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metrics_scd'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.581183 (Thread-1): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.581441 (Thread-1): On model.airbyte_utils.tickets_scd: BEGIN
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.581591 (Thread-1): Opening a new connection, currently in state closed
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.582627 (Thread-1): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.657339 (Thread-1): SQL status: BEGIN in 0.08 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.657646 (Thread-1): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.657779 (Thread-1): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'tickets_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'tickets_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'tickets_scd'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.983127 (Thread-3): SQL status: SELECT in 0.49 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.991593 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:13.991783 (Thread-3): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_fields_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_fields_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_fields_stg'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.067796 (Thread-4): SQL status: SELECT in 0.51 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.086586 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.086805 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metrics_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metrics_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metrics_stg'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.139162 (Thread-1): SQL status: SELECT in 0.48 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.156527 (Thread-1): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.156715 (Thread-1): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'tickets_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'tickets_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'tickets_scd'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.405053 (Thread-3): SQL status: SELECT in 0.41 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.407985 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.408161 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.408341 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.408504 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.408646 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.408785 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.408922 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.409064 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.409198 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.409332 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.409524 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.409663 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.409798 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.409935 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.410069 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.410203 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.410346 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.410496 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.410629 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.410761 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.410892 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.411024 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.411202 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.411338 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.411469 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.411627 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.411761 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.411899 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.416467 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.419509 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.419660 (Thread-3): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_fields_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_fields_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_fields_scd'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.480496 (Thread-2): SQL status: SELECT in 1.10 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.484895 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.485062 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metric_events_scd__dbt_tmp131512877080'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metric_events_scd__dbt_tmp131512877080'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'None'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metric_events_scd__dbt_tmp131512877080'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.586527 (Thread-4): SQL status: SELECT in 0.50 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.589463 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.589641 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.589784 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.589922 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.590051 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.590185 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.590310 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.590432 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.590559 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.590683 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.590804 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.590923 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.591043 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.591164 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.591291 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.591411 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.591532 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.591653 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.591773 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.591897 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.592027 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.592147 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.592295 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.592430 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.592551 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.592672 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.592793 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.592912 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.593030 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.593155 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.593276 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.596189 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.604434 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metrics_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metrics_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metrics_scd'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.665871 (Thread-1): SQL status: SELECT in 0.51 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.675734 (Thread-1): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.675902 (Thread-1): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'tickets_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'tickets_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'tickets_stg'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.876091 (Thread-3): SQL status: SELECT in 0.46 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.879052 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.879231 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.879377 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.879523 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.879661 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.879798 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.879934 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.880070 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.880204 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.880379 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.880515 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.880651 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.880791 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.880925 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.881069 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.881203 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.881335 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.881467 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.881599 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.881729 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.881867 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.882002 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.882135 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.882274 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.882406 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.882540 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.882672 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.882803 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.882934 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.883066 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.888378 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.888538 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.888673 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.888802 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.888964 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.889109 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.889247 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.889379 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.889510 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.889639 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.889771 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.889905 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.890038 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.890170 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:14.890300 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.088444 (Thread-2): SQL status: SELECT in 0.60 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.093494 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.093668 (Thread-4): SQL status: SELECT in 0.49 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.093814 (Thread-1): SQL status: SELECT in 0.42 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.108950 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.109159 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.109303 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.109443 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.109576 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.109702 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.109827 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.109952 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.110075 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.110197 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.110324 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.110446 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.110571 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.110693 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.110815 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.110937 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.111057 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.111177 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.111298 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.111418 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.111538 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.111663 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.111785 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.112114 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.102143 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.112462 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.112594 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.112719 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.112853 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.112975 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.113104 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.113225 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.113347 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.113470 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.113598 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.113718 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.113839 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.113959 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.114079 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.114199 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.114317 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.114443 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.114563 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.114682 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.114802 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.114922 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.115042 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.115162 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.115291 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.115413 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.115535 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.115656 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.115777 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.115903 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.116023 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.116145 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.093990 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.119936 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.120177 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.120365 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.120512 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.120652 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.120819 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.120961 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.121095 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.121230 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.121375 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.121507 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.121644 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.121780 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.121912 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.122044 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.122175 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.122307 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.122442 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.112241 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.134907 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.135058 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.135194 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.135322 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.135450 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.135575 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.135704 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.135827 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.135952 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.136073 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.136196 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.136349 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.136476 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.136602 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.136725 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.139403 (Thread-1): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.139558 (Thread-1): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'tickets_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'tickets_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'tickets_scd'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.119258 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.139881 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.140029 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.140158 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.148392 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.148597 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.148748 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.148890 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.149027 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.149161 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.149295 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.149440 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.149579 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.149708 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.149838 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.149973 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.150104 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.150233 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.150363 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.150494 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.150624 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.150754 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.150887 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.151019 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.151148 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.151276 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.151405 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.151534 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.151663 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.151793 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.151922 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.152050 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.152179 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.156410 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.156590 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.134621 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.160532 (Thread-3): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_fields_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_fields_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_fields_stg'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.160351 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.160830 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metrics_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metrics_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metrics_stg'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.487717 (Thread-2): SQL status: SELECT in 0.37 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.493139 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.493306 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metric_events_scd__dbt_tmp131512877080'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metric_events_scd__dbt_tmp131512877080'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'None'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metric_events_scd__dbt_tmp131512877080'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.550090 (Thread-1): SQL status: SELECT in 0.41 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.554890 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.555074 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.555211 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.555340 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.555464 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.555585 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.555706 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.555828 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.555946 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.556065 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.556184 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.556329 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.556458 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.556577 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.556696 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.556815 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.556935 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.557053 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.557174 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.557294 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.557412 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.557542 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.557663 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.557782 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.557907 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.558026 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.558144 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.558263 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.558390 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.558510 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.558628 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.558747 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.558867 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.558985 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.559104 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.559226 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.559345 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.559463 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.559581 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.559700 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.559818 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.559938 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.560056 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.560175 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.572387 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.572618 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.572774 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.572915 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.573058 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.573195 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.573329 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.573471 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.573604 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.573734 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.573864 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.573994 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.574123 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.574254 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.574383 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.574523 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.574654 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.574784 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.574916 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.575044 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.575498 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.575650 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.575786 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.575923 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.576054 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.576182 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.576350 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.576483 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.576613 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.576743 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.576871 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.576998 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.577124 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.577257 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.577386 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.577513 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.577640 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.577766 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.577900 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.578027 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.578155 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.590158 (Thread-1): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.590336 (Thread-1): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'tickets_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'tickets_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'tickets_stg'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.621775 (Thread-4): SQL status: SELECT in 0.46 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.624749 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.624928 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.625075 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.625206 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.625333 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.625578 (Thread-3): SQL status: SELECT in 0.46 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.627917 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.628080 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.628220 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.631086 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.631257 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.631393 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.631520 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.631645 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.631768 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.631891 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.632017 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.632138 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.632292 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.632425 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.632548 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.632675 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.632797 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.632918 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.633041 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.633163 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.633284 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.633405 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.633525 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.633645 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.633765 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.633885 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.634013 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.634141 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.634262 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.634412 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.634546 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.634675 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.634805 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.634934 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.635063 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.635191 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.635333 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.635464 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.635591 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.635717 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.635841 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.635969 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.636096 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.625758 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.644004 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.644158 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.648453 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.649194 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.649369 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.649505 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.649632 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.649755 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.649877 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.649997 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.650115 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.650235 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.650352 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.650483 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.650609 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.650728 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.650848 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.650966 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.651084 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.651203 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.651322 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.651448 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.651568 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.651685 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.651802 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.651956 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.652091 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.636224 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.660501 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.660662 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.660807 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.660945 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.661080 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.661210 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.661339 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.661470 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.661602 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.661740 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.661869 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.661998 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.662126 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.662255 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.652219 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.670287 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.670448 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.670588 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.670721 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.670852 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.670980 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.671108 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.671235 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.671368 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.671496 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.671622 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.671757 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.671885 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.672012 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.672139 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.680357 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.680596 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.680746 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.680949 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.681158 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.681297 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.681429 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.681560 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.681689 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.681817 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.681943 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.682069 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.682197 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.670078 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.693959 (Thread-3): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_fields_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_fields_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_fields_stg'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.693772 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.694253 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metrics_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metrics_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metrics_stg'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.862752 (Thread-2): SQL status: SELECT in 0.37 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.867884 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.868048 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.934791 (Thread-1): SQL status: SELECT in 0.34 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.937857 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.938035 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.938236 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.938391 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.938523 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.938649 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.938780 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.938902 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.939023 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.939145 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.939264 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.939506 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.939633 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.939754 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.939875 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.939997 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.940120 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.940241 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.940415 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.940541 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.940663 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.940785 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.940913 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.941034 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.941154 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.941273 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.941392 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.941519 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.941640 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.941760 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.941878 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.941998 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.942117 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.942237 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.942356 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.942715 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.942858 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.942990 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.943111 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.943230 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.943376 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.943507 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.943634 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.943760 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.943886 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.944010 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.944135 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.944282 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.944423 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.952374 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.952553 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.952706 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.952844 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.952976 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.953115 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.953244 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.953371 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.953497 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.953626 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.953754 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.953880 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.954014 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.954143 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.954270 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.954398 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.954527 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.954655 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.954783 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.954910 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.955037 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.955164 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.955292 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.955428 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.955558 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.955686 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.955814 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.955941 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.956071 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.956201 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.956368 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.973645 (Thread-1): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:15.973842 (Thread-1): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'tickets_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'tickets_stg'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'tickets_stg'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = '_airbyte_zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.130041 (Thread-3): SQL status: SELECT in 0.44 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.132913 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.133091 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.133233 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.133367 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.133504 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.133634 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.133759 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.133883 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.134005 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.134127 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.134257 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.134378 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.134498 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.134618 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.134738 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.134858 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.134978 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.135098 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.135220 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.135339 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.135466 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.135591 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.135712 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.135830 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.135950 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.136068 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.136190 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.138263 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.138433 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.138602 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.138749 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.138887 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.139022 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.139154 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.139284 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.139429 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.139560 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.139691 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.139825 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.139953 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.140080 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.140206 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.140377 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.140507 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.140635 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.140762 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.141214 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.141372 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.141508 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.141638 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.141766 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.141892 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.142018 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.142144 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.142269 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.142393 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.142525 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.142651 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.157827 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_fields_scd"
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.158218 (Thread-3): finished collecting timing info
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.174031 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.174209 (Thread-3): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_fields_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_fields_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_fields_scd'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.182197 (Thread-4): SQL status: SELECT in 0.49 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.185523 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.185715 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.185855 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.185994 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.186123 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.186250 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.186374 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.186498 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.186620 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.186742 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.186862 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.186990 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.187113 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.187234 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.187361 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.187481 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.187602 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.187723 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.187845 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.187968 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.188087 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.188206 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.188362 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.188491 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.188612 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.188737 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.188857 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.188979 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.189100 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.189220 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.189340 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.189488 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.189624 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.189755 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.189885 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.190014 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.190149 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.190277 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.190411 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.190539 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.190665 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.190799 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.190928 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.191055 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.191183 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.191311 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.191444 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.191573 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.191700 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.191828 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.191958 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.192087 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.192215 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.208539 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.208710 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.208857 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.209319 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.209464 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.209600 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.209739 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.209867 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.209994 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.217052 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_scd"
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.217422 (Thread-4): finished collecting timing info
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.236603 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.236948 (Thread-2): SQL status: SELECT in 0.37 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.245053 (Thread-2): 
2022-04-19 13:15:47 normalization >     In "datalake"."zendesk"."ticket_metric_events_scd":
2022-04-19 13:15:47 normalization >         Schema changed: False
2022-04-19 13:15:47 normalization >         Source columns not in target: []
2022-04-19 13:15:47 normalization >         Target columns not in source: []
2022-04-19 13:15:47 normalization >         New column types: []
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.237142 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metrics_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metrics_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metrics_scd'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.261820 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.262218 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       with bound_views as (
2022-04-19 13:15:47 normalization >         select
2022-04-19 13:15:47 normalization >           ordinal_position,
2022-04-19 13:15:47 normalization >           table_schema,
2022-04-19 13:15:47 normalization >           column_name,
2022-04-19 13:15:47 normalization >           data_type,
2022-04-19 13:15:47 normalization >           character_maximum_length,
2022-04-19 13:15:47 normalization >           numeric_precision,
2022-04-19 13:15:47 normalization >           numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >         from information_schema."columns"
2022-04-19 13:15:47 normalization >         where table_name = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unbound_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         ordinal_position,
2022-04-19 13:15:47 normalization >         view_schema,
2022-04-19 13:15:47 normalization >         col_name,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:47 normalization >             'character varying'
2022-04-19 13:15:47 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else col_type
2022-04-19 13:15:47 normalization >         end as col_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'character%'
2022-04-19 13:15:47 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:47 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:47 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:47 normalization >       where view_name = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     external_views as (
2022-04-19 13:15:47 normalization >       select
2022-04-19 13:15:47 normalization >         columnnum,
2022-04-19 13:15:47 normalization >         schemaname,
2022-04-19 13:15:47 normalization >         columnname,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:47 normalization >           then 'character varying'
2022-04-19 13:15:47 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:47 normalization >           else external_type
2022-04-19 13:15:47 normalization >         end as external_type,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as character_maximum_length,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_precision,
2022-04-19 13:15:47 normalization >         case
2022-04-19 13:15:47 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:47 normalization >           then nullif(
2022-04-19 13:15:47 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:47 normalization >             '')::int
2022-04-19 13:15:47 normalization >           else null
2022-04-19 13:15:47 normalization >         end as numeric_scale
2022-04-19 13:15:47 normalization >       from
2022-04-19 13:15:47 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:47 normalization >       where
2022-04-19 13:15:47 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:47 normalization >         and tablename = 'ticket_metric_events_scd'
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     ),
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     unioned as (
2022-04-19 13:15:47 normalization >       select * from bound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from unbound_views
2022-04-19 13:15:47 normalization >       union all
2022-04-19 13:15:47 normalization >       select * from external_views
2022-04-19 13:15:47 normalization >     )
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     select
2022-04-19 13:15:47 normalization >       column_name,
2022-04-19 13:15:47 normalization >       data_type,
2022-04-19 13:15:47 normalization >       character_maximum_length,
2022-04-19 13:15:47 normalization >       numeric_precision,
2022-04-19 13:15:47 normalization >       numeric_scale
2022-04-19 13:15:47 normalization > 
2022-04-19 13:15:47 normalization >     from unioned
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:47 normalization >     
2022-04-19 13:15:47 normalization >     order by ordinal_position
2022-04-19 13:15:47 normalization >   
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.327628 (Thread-1): SQL status: SELECT in 0.35 seconds
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.332882 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.334090 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.334523 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.334725 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.335087 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.335279 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.335686 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.336053 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.336248 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.336679 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.336884 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.337291 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.338149 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.338298 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.339227 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.339368 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.339505 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.339636 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.339760 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.339883 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.340004 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.340123 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.340245 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.340397 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.340520 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.343479 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:47 normalization > 2022-04-19 13:15:16.343635 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.343862 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.344000 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.344133 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.344282 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.348715 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.348866 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.349068 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.349218 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.349362 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.349493 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.349621 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.349751 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.349880 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.350041 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.350191 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.350327 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.350464 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.350597 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.350731 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.350875 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.351010 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.351144 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.351285 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.351421 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.351561 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.351718 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.351859 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.351993 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.352127 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.359454 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.359676 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.359843 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.359993 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.360137 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.360305 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.360526 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.360667 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.360805 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.361297 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.361438 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.361574 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.361706 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.361836 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.361969 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.362099 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.362228 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.368485 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.369487 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.369884 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.370664 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.371420 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.372178 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.373000 (Thread-1): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.385897 (Thread-1): Writing injected SQL for node "model.airbyte_utils.tickets_scd"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.386302 (Thread-1): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.409276 (Thread-1): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.409515 (Thread-1): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'tickets_scd'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'tickets_scd'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'tickets_scd'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.522135 (Thread-3): SQL status: SELECT in 0.35 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.528429 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.528632 (Thread-3): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >   create temporary table
2022-04-19 13:15:48 normalization >     "ticket_fields_scd__dbt_tmp131516170906"
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >       compound sortkey(_airbyte_active_row,_airbyte_unique_key_scd,_airbyte_emitted_at)
2022-04-19 13:15:48 normalization >   as (
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > -- depends_on: ref('ticket_fields_stg')
2022-04-19 13:15:48 normalization > with
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > new_data as (
2022-04-19 13:15:48 normalization >     -- retrieve incremental "new" data
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >         *
2022-04-19 13:15:48 normalization >     from "datalake"._airbyte_zendesk."ticket_fields_stg"
2022-04-19 13:15:48 normalization >     -- ticket_fields from "datalake".zendesk._airbyte_raw_ticket_fields
2022-04-19 13:15:48 normalization >     where 1 = 1
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > and coalesce(
2022-04-19 13:15:48 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:48 normalization >     timestamp with time zone
2022-04-19 13:15:48 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:48 normalization >     timestamp with time zone
2022-04-19 13:15:48 normalization > )) from "datalake".zendesk."ticket_fields_scd"),
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     true)
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > new_data_ids as (
2022-04-19 13:15:48 normalization >     -- build a subset of _airbyte_unique_key from rows that are new
2022-04-19 13:15:48 normalization >     select distinct
2022-04-19 13:15:48 normalization >         md5(cast(coalesce(cast(id as varchar), '') as varchar)) as _airbyte_unique_key
2022-04-19 13:15:48 normalization >     from new_data
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > empty_new_data as (
2022-04-19 13:15:48 normalization >     -- build an empty table to only keep the table's column types
2022-04-19 13:15:48 normalization >     select * from new_data where 1 = 0
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > previous_active_scd_data as (
2022-04-19 13:15:48 normalization >     -- retrieve "incomplete old" data that needs to be updated with an end date because of new changes
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >         this_data."_airbyte_ticket_fields_hashid",
2022-04-19 13:15:48 normalization >   this_data."id",
2022-04-19 13:15:48 normalization >   this_data."tag",
2022-04-19 13:15:48 normalization >   this_data."url",
2022-04-19 13:15:48 normalization >   this_data."type",
2022-04-19 13:15:48 normalization >   this_data."title",
2022-04-19 13:15:48 normalization >   this_data."active",
2022-04-19 13:15:48 normalization >   this_data."position",
2022-04-19 13:15:48 normalization >   this_data."required",
2022-04-19 13:15:48 normalization >   this_data."raw_title",
2022-04-19 13:15:48 normalization >   this_data."removable",
2022-04-19 13:15:48 normalization >   this_data."created_at",
2022-04-19 13:15:48 normalization >   this_data."updated_at",
2022-04-19 13:15:48 normalization >   this_data."description",
2022-04-19 13:15:48 normalization >   this_data."sub_type_id",
2022-04-19 13:15:48 normalization >   this_data."raw_description",
2022-04-19 13:15:48 normalization >   this_data."title_in_portal",
2022-04-19 13:15:48 normalization >   this_data."agent_description",
2022-04-19 13:15:48 normalization >   this_data."visible_in_portal",
2022-04-19 13:15:48 normalization >   this_data."editable_in_portal",
2022-04-19 13:15:48 normalization >   this_data."required_in_portal",
2022-04-19 13:15:48 normalization >   this_data."raw_title_in_portal",
2022-04-19 13:15:48 normalization >   this_data."collapsed_for_agents",
2022-04-19 13:15:48 normalization >   this_data."custom_field_options",
2022-04-19 13:15:48 normalization >   this_data."system_field_options",
2022-04-19 13:15:48 normalization >   this_data."regexp_for_validation",
2022-04-19 13:15:48 normalization >   this_data."_airbyte_ab_id",
2022-04-19 13:15:48 normalization >   this_data."_airbyte_emitted_at",
2022-04-19 13:15:48 normalization >   this_data."_airbyte_normalized_at"
2022-04-19 13:15:48 normalization >     from "datalake".zendesk."ticket_fields_scd" as this_data
2022-04-19 13:15:48 normalization >     -- make a join with new_data using primary key to filter active data that need to be updated only
2022-04-19 13:15:48 normalization >     join new_data_ids on this_data._airbyte_unique_key = new_data_ids._airbyte_unique_key
2022-04-19 13:15:48 normalization >     -- force left join to NULL values (we just need to transfer column types only for the star_intersect macro on schema changes)
2022-04-19 13:15:48 normalization >     left join empty_new_data as inc_data on this_data._airbyte_ab_id = inc_data._airbyte_ab_id
2022-04-19 13:15:48 normalization >     where _airbyte_active_row = 1
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > input_data as (
2022-04-19 13:15:48 normalization >     select "_airbyte_ticket_fields_hashid",
2022-04-19 13:15:48 normalization >   "id",
2022-04-19 13:15:48 normalization >   "tag",
2022-04-19 13:15:48 normalization >   "url",
2022-04-19 13:15:48 normalization >   "type",
2022-04-19 13:15:48 normalization >   "title",
2022-04-19 13:15:48 normalization >   "active",
2022-04-19 13:15:48 normalization >   "position",
2022-04-19 13:15:48 normalization >   "required",
2022-04-19 13:15:48 normalization >   "raw_title",
2022-04-19 13:15:48 normalization >   "removable",
2022-04-19 13:15:48 normalization >   "created_at",
2022-04-19 13:15:48 normalization >   "updated_at",
2022-04-19 13:15:48 normalization >   "description",
2022-04-19 13:15:48 normalization >   "sub_type_id",
2022-04-19 13:15:48 normalization >   "raw_description",
2022-04-19 13:15:48 normalization >   "title_in_portal",
2022-04-19 13:15:48 normalization >   "agent_description",
2022-04-19 13:15:48 normalization >   "visible_in_portal",
2022-04-19 13:15:48 normalization >   "editable_in_portal",
2022-04-19 13:15:48 normalization >   "required_in_portal",
2022-04-19 13:15:48 normalization >   "raw_title_in_portal",
2022-04-19 13:15:48 normalization >   "collapsed_for_agents",
2022-04-19 13:15:48 normalization >   "custom_field_options",
2022-04-19 13:15:48 normalization >   "system_field_options",
2022-04-19 13:15:48 normalization >   "regexp_for_validation",
2022-04-19 13:15:48 normalization >   "_airbyte_ab_id",
2022-04-19 13:15:48 normalization >   "_airbyte_emitted_at",
2022-04-19 13:15:48 normalization >   "_airbyte_normalized_at" from new_data
2022-04-19 13:15:48 normalization >     union all
2022-04-19 13:15:48 normalization >     select "_airbyte_ticket_fields_hashid",
2022-04-19 13:15:48 normalization >   "id",
2022-04-19 13:15:48 normalization >   "tag",
2022-04-19 13:15:48 normalization >   "url",
2022-04-19 13:15:48 normalization >   "type",
2022-04-19 13:15:48 normalization >   "title",
2022-04-19 13:15:48 normalization >   "active",
2022-04-19 13:15:48 normalization >   "position",
2022-04-19 13:15:48 normalization >   "required",
2022-04-19 13:15:48 normalization >   "raw_title",
2022-04-19 13:15:48 normalization >   "removable",
2022-04-19 13:15:48 normalization >   "created_at",
2022-04-19 13:15:48 normalization >   "updated_at",
2022-04-19 13:15:48 normalization >   "description",
2022-04-19 13:15:48 normalization >   "sub_type_id",
2022-04-19 13:15:48 normalization >   "raw_description",
2022-04-19 13:15:48 normalization >   "title_in_portal",
2022-04-19 13:15:48 normalization >   "agent_description",
2022-04-19 13:15:48 normalization >   "visible_in_portal",
2022-04-19 13:15:48 normalization >   "editable_in_portal",
2022-04-19 13:15:48 normalization >   "required_in_portal",
2022-04-19 13:15:48 normalization >   "raw_title_in_portal",
2022-04-19 13:15:48 normalization >   "collapsed_for_agents",
2022-04-19 13:15:48 normalization >   "custom_field_options",
2022-04-19 13:15:48 normalization >   "system_field_options",
2022-04-19 13:15:48 normalization >   "regexp_for_validation",
2022-04-19 13:15:48 normalization >   "_airbyte_ab_id",
2022-04-19 13:15:48 normalization >   "_airbyte_emitted_at",
2022-04-19 13:15:48 normalization >   "_airbyte_normalized_at" from previous_active_scd_data
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > scd_data as (
2022-04-19 13:15:48 normalization >     -- SQL model to build a Type 2 Slowly Changing Dimension (SCD) table for each record identified by their primary key
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       md5(cast(coalesce(cast(id as varchar), '') as varchar)) as _airbyte_unique_key,
2022-04-19 13:15:48 normalization >       id,
2022-04-19 13:15:48 normalization >       "tag",
2022-04-19 13:15:48 normalization >       url,
2022-04-19 13:15:48 normalization >       type,
2022-04-19 13:15:48 normalization >       title,
2022-04-19 13:15:48 normalization >       active,
2022-04-19 13:15:48 normalization >       position,
2022-04-19 13:15:48 normalization >       required,
2022-04-19 13:15:48 normalization >       raw_title,
2022-04-19 13:15:48 normalization >       removable,
2022-04-19 13:15:48 normalization >       created_at,
2022-04-19 13:15:48 normalization >       updated_at,
2022-04-19 13:15:48 normalization >       description,
2022-04-19 13:15:48 normalization >       sub_type_id,
2022-04-19 13:15:48 normalization >       raw_description,
2022-04-19 13:15:48 normalization >       title_in_portal,
2022-04-19 13:15:48 normalization >       agent_description,
2022-04-19 13:15:48 normalization >       visible_in_portal,
2022-04-19 13:15:48 normalization >       editable_in_portal,
2022-04-19 13:15:48 normalization >       required_in_portal,
2022-04-19 13:15:48 normalization >       raw_title_in_portal,
2022-04-19 13:15:48 normalization >       collapsed_for_agents,
2022-04-19 13:15:48 normalization >       custom_field_options,
2022-04-19 13:15:48 normalization >       system_field_options,
2022-04-19 13:15:48 normalization >       regexp_for_validation,
2022-04-19 13:15:48 normalization >       updated_at as _airbyte_start_at,
2022-04-19 13:15:48 normalization >       lag(updated_at) over (
2022-04-19 13:15:48 normalization >         partition by id
2022-04-19 13:15:48 normalization >         order by
2022-04-19 13:15:48 normalization >             updated_at is null asc,
2022-04-19 13:15:48 normalization >             updated_at desc,
2022-04-19 13:15:48 normalization >             _airbyte_emitted_at desc
2022-04-19 13:15:48 normalization >       ) as _airbyte_end_at,
2022-04-19 13:15:48 normalization >       case when row_number() over (
2022-04-19 13:15:48 normalization >         partition by id
2022-04-19 13:15:48 normalization >         order by
2022-04-19 13:15:48 normalization >             updated_at is null asc,
2022-04-19 13:15:48 normalization >             updated_at desc,
2022-04-19 13:15:48 normalization >             _airbyte_emitted_at desc
2022-04-19 13:15:48 normalization >       ) = 1 then 1 else 0 end as _airbyte_active_row,
2022-04-19 13:15:48 normalization >       _airbyte_ab_id,
2022-04-19 13:15:48 normalization >       _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >       _airbyte_ticket_fields_hashid
2022-04-19 13:15:48 normalization >     from input_data
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > dedup_data as (
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >         -- we need to ensure de-duplicated rows for merge/update queries
2022-04-19 13:15:48 normalization >         -- additionally, we generate a unique key for the scd table
2022-04-19 13:15:48 normalization >         row_number() over (
2022-04-19 13:15:48 normalization >             partition by
2022-04-19 13:15:48 normalization >                 _airbyte_unique_key,
2022-04-19 13:15:48 normalization >                 _airbyte_start_at,
2022-04-19 13:15:48 normalization >                 _airbyte_emitted_at
2022-04-19 13:15:48 normalization >             order by _airbyte_active_row desc, _airbyte_ab_id
2022-04-19 13:15:48 normalization >         ) as _airbyte_row_num,
2022-04-19 13:15:48 normalization >         md5(cast(coalesce(cast(_airbyte_unique_key as varchar), '') || '-' || coalesce(cast(_airbyte_start_at as varchar), '') || '-' || coalesce(cast(_airbyte_emitted_at as varchar), '') as varchar)) as _airbyte_unique_key_scd,
2022-04-19 13:15:48 normalization >         scd_data.*
2022-04-19 13:15:48 normalization >     from scd_data
2022-04-19 13:15:48 normalization > )
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     _airbyte_unique_key,
2022-04-19 13:15:48 normalization >     _airbyte_unique_key_scd,
2022-04-19 13:15:48 normalization >     id,
2022-04-19 13:15:48 normalization >     "tag",
2022-04-19 13:15:48 normalization >     url,
2022-04-19 13:15:48 normalization >     type,
2022-04-19 13:15:48 normalization >     title,
2022-04-19 13:15:48 normalization >     active,
2022-04-19 13:15:48 normalization >     position,
2022-04-19 13:15:48 normalization >     required,
2022-04-19 13:15:48 normalization >     raw_title,
2022-04-19 13:15:48 normalization >     removable,
2022-04-19 13:15:48 normalization >     created_at,
2022-04-19 13:15:48 normalization >     updated_at,
2022-04-19 13:15:48 normalization >     description,
2022-04-19 13:15:48 normalization >     sub_type_id,
2022-04-19 13:15:48 normalization >     raw_description,
2022-04-19 13:15:48 normalization >     title_in_portal,
2022-04-19 13:15:48 normalization >     agent_description,
2022-04-19 13:15:48 normalization >     visible_in_portal,
2022-04-19 13:15:48 normalization >     editable_in_portal,
2022-04-19 13:15:48 normalization >     required_in_portal,
2022-04-19 13:15:48 normalization >     raw_title_in_portal,
2022-04-19 13:15:48 normalization >     collapsed_for_agents,
2022-04-19 13:15:48 normalization >     custom_field_options,
2022-04-19 13:15:48 normalization >     system_field_options,
2022-04-19 13:15:48 normalization >     regexp_for_validation,
2022-04-19 13:15:48 normalization >     _airbyte_start_at,
2022-04-19 13:15:48 normalization >     _airbyte_end_at,
2022-04-19 13:15:48 normalization >     _airbyte_active_row,
2022-04-19 13:15:48 normalization >     _airbyte_ab_id,
2022-04-19 13:15:48 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:48 normalization >     _airbyte_ticket_fields_hashid
2022-04-19 13:15:48 normalization > from dedup_data where _airbyte_row_num = 1
2022-04-19 13:15:48 normalization >   );
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.663998 (Thread-4): SQL status: SELECT in 0.40 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.670506 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.672835 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >   create temporary table
2022-04-19 13:15:48 normalization >     "ticket_metrics_scd__dbt_tmp131516226018"
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >       compound sortkey(_airbyte_active_row,_airbyte_unique_key_scd,_airbyte_emitted_at)
2022-04-19 13:15:48 normalization >   as (
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > -- depends_on: ref('ticket_metrics_stg')
2022-04-19 13:15:48 normalization > with
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > new_data as (
2022-04-19 13:15:48 normalization >     -- retrieve incremental "new" data
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >         *
2022-04-19 13:15:48 normalization >     from "datalake"._airbyte_zendesk."ticket_metrics_stg"
2022-04-19 13:15:48 normalization >     -- ticket_metrics from "datalake".zendesk._airbyte_raw_ticket_metrics
2022-04-19 13:15:48 normalization >     where 1 = 1
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > and coalesce(
2022-04-19 13:15:48 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:48 normalization >     timestamp with time zone
2022-04-19 13:15:48 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:48 normalization >     timestamp with time zone
2022-04-19 13:15:48 normalization > )) from "datalake".zendesk."ticket_metrics_scd"),
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     true)
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > new_data_ids as (
2022-04-19 13:15:48 normalization >     -- build a subset of _airbyte_unique_key from rows that are new
2022-04-19 13:15:48 normalization >     select distinct
2022-04-19 13:15:48 normalization >         md5(cast(coalesce(cast(id as varchar), '') as varchar)) as _airbyte_unique_key
2022-04-19 13:15:48 normalization >     from new_data
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > empty_new_data as (
2022-04-19 13:15:48 normalization >     -- build an empty table to only keep the table's column types
2022-04-19 13:15:48 normalization >     select * from new_data where 1 = 0
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > previous_active_scd_data as (
2022-04-19 13:15:48 normalization >     -- retrieve "incomplete old" data that needs to be updated with an end date because of new changes
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >         this_data."_airbyte_ticket_metrics_hashid",
2022-04-19 13:15:48 normalization >   this_data."id",
2022-04-19 13:15:48 normalization >   this_data."url",
2022-04-19 13:15:48 normalization >   this_data."time",
2022-04-19 13:15:48 normalization >   this_data."type",
2022-04-19 13:15:48 normalization >   this_data."metric",
2022-04-19 13:15:48 normalization >   this_data."status",
2022-04-19 13:15:48 normalization >   this_data."reopens",
2022-04-19 13:15:48 normalization >   this_data."replies",
2022-04-19 13:15:48 normalization >   this_data."solved_at",
2022-04-19 13:15:48 normalization >   this_data."ticket_id",
2022-04-19 13:15:48 normalization >   this_data."created_at",
2022-04-19 13:15:48 normalization >   this_data."updated_at",
2022-04-19 13:15:48 normalization >   this_data."assigned_at",
2022-04-19 13:15:48 normalization >   this_data."instance_id",
2022-04-19 13:15:48 normalization >   this_data."group_stations",
2022-04-19 13:15:48 normalization >   this_data."assignee_stations",
2022-04-19 13:15:48 normalization >   this_data."status_updated_at",
2022-04-19 13:15:48 normalization >   this_data."assignee_updated_at",
2022-04-19 13:15:48 normalization >   this_data."requester_updated_at",
2022-04-19 13:15:48 normalization >   this_data."initially_assigned_at",
2022-04-19 13:15:48 normalization >   this_data."reply_time_in_minutes",
2022-04-19 13:15:48 normalization >   this_data."latest_comment_added_at",
2022-04-19 13:15:48 normalization >   this_data."on_hold_time_in_minutes",
2022-04-19 13:15:48 normalization >   this_data."agent_wait_time_in_minutes",
2022-04-19 13:15:48 normalization >   this_data."requester_wait_time_in_minutes",
2022-04-19 13:15:48 normalization >   this_data."full_resolution_time_in_minutes",
2022-04-19 13:15:48 normalization >   this_data."first_resolution_time_in_minutes",
2022-04-19 13:15:48 normalization >   this_data."_airbyte_ab_id",
2022-04-19 13:15:48 normalization >   this_data."_airbyte_emitted_at",
2022-04-19 13:15:48 normalization >   this_data."_airbyte_normalized_at"
2022-04-19 13:15:48 normalization >     from "datalake".zendesk."ticket_metrics_scd" as this_data
2022-04-19 13:15:48 normalization >     -- make a join with new_data using primary key to filter active data that need to be updated only
2022-04-19 13:15:48 normalization >     join new_data_ids on this_data._airbyte_unique_key = new_data_ids._airbyte_unique_key
2022-04-19 13:15:48 normalization >     -- force left join to NULL values (we just need to transfer column types only for the star_intersect macro on schema changes)
2022-04-19 13:15:48 normalization >     left join empty_new_data as inc_data on this_data._airbyte_ab_id = inc_data._airbyte_ab_id
2022-04-19 13:15:48 normalization >     where _airbyte_active_row = 1
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > input_data as (
2022-04-19 13:15:48 normalization >     select "_airbyte_ticket_metrics_hashid",
2022-04-19 13:15:48 normalization >   "id",
2022-04-19 13:15:48 normalization >   "url",
2022-04-19 13:15:48 normalization >   "time",
2022-04-19 13:15:48 normalization >   "type",
2022-04-19 13:15:48 normalization >   "metric",
2022-04-19 13:15:48 normalization >   "status",
2022-04-19 13:15:48 normalization >   "reopens",
2022-04-19 13:15:48 normalization >   "replies",
2022-04-19 13:15:48 normalization >   "solved_at",
2022-04-19 13:15:48 normalization >   "ticket_id",
2022-04-19 13:15:48 normalization >   "created_at",
2022-04-19 13:15:48 normalization >   "updated_at",
2022-04-19 13:15:48 normalization >   "assigned_at",
2022-04-19 13:15:48 normalization >   "instance_id",
2022-04-19 13:15:48 normalization >   "group_stations",
2022-04-19 13:15:48 normalization >   "assignee_stations",
2022-04-19 13:15:48 normalization >   "status_updated_at",
2022-04-19 13:15:48 normalization >   "assignee_updated_at",
2022-04-19 13:15:48 normalization >   "requester_updated_at",
2022-04-19 13:15:48 normalization >   "initially_assigned_at",
2022-04-19 13:15:48 normalization >   "reply_time_in_minutes",
2022-04-19 13:15:48 normalization >   "latest_comment_added_at",
2022-04-19 13:15:48 normalization >   "on_hold_time_in_minutes",
2022-04-19 13:15:48 normalization >   "agent_wait_time_in_minutes",
2022-04-19 13:15:48 normalization >   "requester_wait_time_in_minutes",
2022-04-19 13:15:48 normalization >   "full_resolution_time_in_minutes",
2022-04-19 13:15:48 normalization >   "first_resolution_time_in_minutes",
2022-04-19 13:15:48 normalization >   "_airbyte_ab_id",
2022-04-19 13:15:48 normalization >   "_airbyte_emitted_at",
2022-04-19 13:15:48 normalization >   "_airbyte_normalized_at" from new_data
2022-04-19 13:15:48 normalization >     union all
2022-04-19 13:15:48 normalization >     select "_airbyte_ticket_metrics_hashid",
2022-04-19 13:15:48 normalization >   "id",
2022-04-19 13:15:48 normalization >   "url",
2022-04-19 13:15:48 normalization >   "time",
2022-04-19 13:15:48 normalization >   "type",
2022-04-19 13:15:48 normalization >   "metric",
2022-04-19 13:15:48 normalization >   "status",
2022-04-19 13:15:48 normalization >   "reopens",
2022-04-19 13:15:48 normalization >   "replies",
2022-04-19 13:15:48 normalization >   "solved_at",
2022-04-19 13:15:48 normalization >   "ticket_id",
2022-04-19 13:15:48 normalization >   "created_at",
2022-04-19 13:15:48 normalization >   "updated_at",
2022-04-19 13:15:48 normalization >   "assigned_at",
2022-04-19 13:15:48 normalization >   "instance_id",
2022-04-19 13:15:48 normalization >   "group_stations",
2022-04-19 13:15:48 normalization >   "assignee_stations",
2022-04-19 13:15:48 normalization >   "status_updated_at",
2022-04-19 13:15:48 normalization >   "assignee_updated_at",
2022-04-19 13:15:48 normalization >   "requester_updated_at",
2022-04-19 13:15:48 normalization >   "initially_assigned_at",
2022-04-19 13:15:48 normalization >   "reply_time_in_minutes",
2022-04-19 13:15:48 normalization >   "latest_comment_added_at",
2022-04-19 13:15:48 normalization >   "on_hold_time_in_minutes",
2022-04-19 13:15:48 normalization >   "agent_wait_time_in_minutes",
2022-04-19 13:15:48 normalization >   "requester_wait_time_in_minutes",
2022-04-19 13:15:48 normalization >   "full_resolution_time_in_minutes",
2022-04-19 13:15:48 normalization >   "first_resolution_time_in_minutes",
2022-04-19 13:15:48 normalization >   "_airbyte_ab_id",
2022-04-19 13:15:48 normalization >   "_airbyte_emitted_at",
2022-04-19 13:15:48 normalization >   "_airbyte_normalized_at" from previous_active_scd_data
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > scd_data as (
2022-04-19 13:15:48 normalization >     -- SQL model to build a Type 2 Slowly Changing Dimension (SCD) table for each record identified by their primary key
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       md5(cast(coalesce(cast(id as varchar), '') as varchar)) as _airbyte_unique_key,
2022-04-19 13:15:48 normalization >       id,
2022-04-19 13:15:48 normalization >       url,
2022-04-19 13:15:48 normalization >       "time",
2022-04-19 13:15:48 normalization >       type,
2022-04-19 13:15:48 normalization >       metric,
2022-04-19 13:15:48 normalization >       status,
2022-04-19 13:15:48 normalization >       reopens,
2022-04-19 13:15:48 normalization >       replies,
2022-04-19 13:15:48 normalization >       solved_at,
2022-04-19 13:15:48 normalization >       ticket_id,
2022-04-19 13:15:48 normalization >       created_at,
2022-04-19 13:15:48 normalization >       updated_at,
2022-04-19 13:15:48 normalization >       assigned_at,
2022-04-19 13:15:48 normalization >       instance_id,
2022-04-19 13:15:48 normalization >       group_stations,
2022-04-19 13:15:48 normalization >       assignee_stations,
2022-04-19 13:15:48 normalization >       status_updated_at,
2022-04-19 13:15:48 normalization >       assignee_updated_at,
2022-04-19 13:15:48 normalization >       requester_updated_at,
2022-04-19 13:15:48 normalization >       initially_assigned_at,
2022-04-19 13:15:48 normalization >       reply_time_in_minutes,
2022-04-19 13:15:48 normalization >       latest_comment_added_at,
2022-04-19 13:15:48 normalization >       on_hold_time_in_minutes,
2022-04-19 13:15:48 normalization >       agent_wait_time_in_minutes,
2022-04-19 13:15:48 normalization >       requester_wait_time_in_minutes,
2022-04-19 13:15:48 normalization >       full_resolution_time_in_minutes,
2022-04-19 13:15:48 normalization >       first_resolution_time_in_minutes,
2022-04-19 13:15:48 normalization >       updated_at as _airbyte_start_at,
2022-04-19 13:15:48 normalization >       lag(updated_at) over (
2022-04-19 13:15:48 normalization >         partition by id
2022-04-19 13:15:48 normalization >         order by
2022-04-19 13:15:48 normalization >             updated_at is null asc,
2022-04-19 13:15:48 normalization >             updated_at desc,
2022-04-19 13:15:48 normalization >             _airbyte_emitted_at desc
2022-04-19 13:15:48 normalization >       ) as _airbyte_end_at,
2022-04-19 13:15:48 normalization >       case when row_number() over (
2022-04-19 13:15:48 normalization >         partition by id
2022-04-19 13:15:48 normalization >         order by
2022-04-19 13:15:48 normalization >             updated_at is null asc,
2022-04-19 13:15:48 normalization >             updated_at desc,
2022-04-19 13:15:48 normalization >             _airbyte_emitted_at desc
2022-04-19 13:15:48 normalization >       ) = 1 then 1 else 0 end as _airbyte_active_row,
2022-04-19 13:15:48 normalization >       _airbyte_ab_id,
2022-04-19 13:15:48 normalization >       _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >       _airbyte_ticket_metrics_hashid
2022-04-19 13:15:48 normalization >     from input_data
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > dedup_data as (
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >         -- we need to ensure de-duplicated rows for merge/update queries
2022-04-19 13:15:48 normalization >         -- additionally, we generate a unique key for the scd table
2022-04-19 13:15:48 normalization >         row_number() over (
2022-04-19 13:15:48 normalization >             partition by
2022-04-19 13:15:48 normalization >                 _airbyte_unique_key,
2022-04-19 13:15:48 normalization >                 _airbyte_start_at,
2022-04-19 13:15:48 normalization >                 _airbyte_emitted_at
2022-04-19 13:15:48 normalization >             order by _airbyte_active_row desc, _airbyte_ab_id
2022-04-19 13:15:48 normalization >         ) as _airbyte_row_num,
2022-04-19 13:15:48 normalization >         md5(cast(coalesce(cast(_airbyte_unique_key as varchar), '') || '-' || coalesce(cast(_airbyte_start_at as varchar), '') || '-' || coalesce(cast(_airbyte_emitted_at as varchar), '') as varchar)) as _airbyte_unique_key_scd,
2022-04-19 13:15:48 normalization >         scd_data.*
2022-04-19 13:15:48 normalization >     from scd_data
2022-04-19 13:15:48 normalization > )
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     _airbyte_unique_key,
2022-04-19 13:15:48 normalization >     _airbyte_unique_key_scd,
2022-04-19 13:15:48 normalization >     id,
2022-04-19 13:15:48 normalization >     url,
2022-04-19 13:15:48 normalization >     "time",
2022-04-19 13:15:48 normalization >     type,
2022-04-19 13:15:48 normalization >     metric,
2022-04-19 13:15:48 normalization >     status,
2022-04-19 13:15:48 normalization >     reopens,
2022-04-19 13:15:48 normalization >     replies,
2022-04-19 13:15:48 normalization >     solved_at,
2022-04-19 13:15:48 normalization >     ticket_id,
2022-04-19 13:15:48 normalization >     created_at,
2022-04-19 13:15:48 normalization >     updated_at,
2022-04-19 13:15:48 normalization >     assigned_at,
2022-04-19 13:15:48 normalization >     instance_id,
2022-04-19 13:15:48 normalization >     group_stations,
2022-04-19 13:15:48 normalization >     assignee_stations,
2022-04-19 13:15:48 normalization >     status_updated_at,
2022-04-19 13:15:48 normalization >     assignee_updated_at,
2022-04-19 13:15:48 normalization >     requester_updated_at,
2022-04-19 13:15:48 normalization >     initially_assigned_at,
2022-04-19 13:15:48 normalization >     reply_time_in_minutes,
2022-04-19 13:15:48 normalization >     latest_comment_added_at,
2022-04-19 13:15:48 normalization >     on_hold_time_in_minutes,
2022-04-19 13:15:48 normalization >     agent_wait_time_in_minutes,
2022-04-19 13:15:48 normalization >     requester_wait_time_in_minutes,
2022-04-19 13:15:48 normalization >     full_resolution_time_in_minutes,
2022-04-19 13:15:48 normalization >     first_resolution_time_in_minutes,
2022-04-19 13:15:48 normalization >     _airbyte_start_at,
2022-04-19 13:15:48 normalization >     _airbyte_end_at,
2022-04-19 13:15:48 normalization >     _airbyte_active_row,
2022-04-19 13:15:48 normalization >     _airbyte_ab_id,
2022-04-19 13:15:48 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:48 normalization >     _airbyte_ticket_metrics_hashid
2022-04-19 13:15:48 normalization > from dedup_data where _airbyte_row_num = 1
2022-04-19 13:15:48 normalization >   );
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.710930 (Thread-2): SQL status: SELECT in 0.45 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.714093 (Thread-2): Writing runtime SQL for node "model.airbyte_utils.ticket_metric_events_scd"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.714586 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.714728 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       delete
2022-04-19 13:15:48 normalization >     from "datalake".zendesk."ticket_metric_events_scd"
2022-04-19 13:15:48 normalization >     where (_airbyte_unique_key_scd) in (
2022-04-19 13:15:48 normalization >         select (_airbyte_unique_key_scd)
2022-04-19 13:15:48 normalization >         from "ticket_metric_events_scd__dbt_tmp131512877080"
2022-04-19 13:15:48 normalization >     );
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     insert into "datalake".zendesk."ticket_metric_events_scd" ("_airbyte_unique_key", "_airbyte_unique_key_scd", "id", "time", "type", "metric", "ticket_id", "instance_id", "_airbyte_start_at", "_airbyte_end_at", "_airbyte_active_row", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_metric_events_hashid")
2022-04-19 13:15:48 normalization >     (
2022-04-19 13:15:48 normalization >        select "_airbyte_unique_key", "_airbyte_unique_key_scd", "id", "time", "type", "metric", "ticket_id", "instance_id", "_airbyte_start_at", "_airbyte_end_at", "_airbyte_active_row", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_metric_events_hashid"
2022-04-19 13:15:48 normalization >        from "ticket_metric_events_scd__dbt_tmp131512877080"
2022-04-19 13:15:48 normalization >     );
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.889918 (Thread-1): SQL status: SELECT in 0.48 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.897950 (Thread-1): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:16.898410 (Thread-1): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >   create temporary table
2022-04-19 13:15:48 normalization >     "tickets_scd__dbt_tmp131516397949"
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >       compound sortkey(_airbyte_active_row,_airbyte_unique_key_scd,_airbyte_emitted_at)
2022-04-19 13:15:48 normalization >   as (
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > -- depends_on: ref('tickets_stg')
2022-04-19 13:15:48 normalization > with
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > new_data as (
2022-04-19 13:15:48 normalization >     -- retrieve incremental "new" data
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >         *
2022-04-19 13:15:48 normalization >     from "datalake"._airbyte_zendesk."tickets_stg"
2022-04-19 13:15:48 normalization >     -- tickets from "datalake".zendesk._airbyte_raw_tickets
2022-04-19 13:15:48 normalization >     where 1 = 1
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > and coalesce(
2022-04-19 13:15:48 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:48 normalization >     timestamp with time zone
2022-04-19 13:15:48 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:48 normalization >     timestamp with time zone
2022-04-19 13:15:48 normalization > )) from "datalake".zendesk."tickets_scd"),
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     true)
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > new_data_ids as (
2022-04-19 13:15:48 normalization >     -- build a subset of _airbyte_unique_key from rows that are new
2022-04-19 13:15:48 normalization >     select distinct
2022-04-19 13:15:48 normalization >         md5(cast(coalesce(cast(id as varchar), '') as varchar)) as _airbyte_unique_key
2022-04-19 13:15:48 normalization >     from new_data
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > empty_new_data as (
2022-04-19 13:15:48 normalization >     -- build an empty table to only keep the table's column types
2022-04-19 13:15:48 normalization >     select * from new_data where 1 = 0
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > previous_active_scd_data as (
2022-04-19 13:15:48 normalization >     -- retrieve "incomplete old" data that needs to be updated with an end date because of new changes
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >         this_data."_airbyte_tickets_hashid",
2022-04-19 13:15:48 normalization >   this_data."id",
2022-04-19 13:15:48 normalization >   this_data."url",
2022-04-19 13:15:48 normalization >   this_data."via",
2022-04-19 13:15:48 normalization >   this_data."tags",
2022-04-19 13:15:48 normalization >   this_data."type",
2022-04-19 13:15:48 normalization >   this_data."due_at",
2022-04-19 13:15:48 normalization >   this_data."status",
2022-04-19 13:15:48 normalization >   this_data."subject",
2022-04-19 13:15:48 normalization >   this_data."brand_id",
2022-04-19 13:15:48 normalization >   this_data."group_id",
2022-04-19 13:15:48 normalization >   this_data."priority",
2022-04-19 13:15:48 normalization >   this_data."is_public",
2022-04-19 13:15:48 normalization >   this_data."recipient",
2022-04-19 13:15:48 normalization >   this_data."created_at",
2022-04-19 13:15:48 normalization >   this_data."problem_id",
2022-04-19 13:15:48 normalization >   this_data."updated_at",
2022-04-19 13:15:48 normalization >   this_data."assignee_id",
2022-04-19 13:15:48 normalization >   this_data."description",
2022-04-19 13:15:48 normalization >   this_data."external_id",
2022-04-19 13:15:48 normalization >   this_data."raw_subject",
2022-04-19 13:15:48 normalization >   this_data."email_cc_ids",
2022-04-19 13:15:48 normalization >   this_data."follower_ids",
2022-04-19 13:15:48 normalization >   this_data."followup_ids",
2022-04-19 13:15:48 normalization >   this_data."requester_id",
2022-04-19 13:15:48 normalization >   this_data."submitter_id",
2022-04-19 13:15:48 normalization >   this_data."custom_fields",
2022-04-19 13:15:48 normalization >   this_data."has_incidents",
2022-04-19 13:15:48 normalization >   this_data."forum_topic_id",
2022-04-19 13:15:48 normalization >   this_data."ticket_form_id",
2022-04-19 13:15:48 normalization >   this_data."organization_id",
2022-04-19 13:15:48 normalization >   this_data."collaborator_ids",
2022-04-19 13:15:48 normalization >   this_data."allow_attachments",
2022-04-19 13:15:48 normalization >   this_data."allow_channelback",
2022-04-19 13:15:48 normalization >   this_data."generated_timestamp",
2022-04-19 13:15:48 normalization >   this_data."satisfaction_rating",
2022-04-19 13:15:48 normalization >   this_data."sharing_agreement_ids",
2022-04-19 13:15:48 normalization >   this_data."_airbyte_ab_id",
2022-04-19 13:15:48 normalization >   this_data."_airbyte_emitted_at",
2022-04-19 13:15:48 normalization >   this_data."_airbyte_normalized_at"
2022-04-19 13:15:48 normalization >     from "datalake".zendesk."tickets_scd" as this_data
2022-04-19 13:15:48 normalization >     -- make a join with new_data using primary key to filter active data that need to be updated only
2022-04-19 13:15:48 normalization >     join new_data_ids on this_data._airbyte_unique_key = new_data_ids._airbyte_unique_key
2022-04-19 13:15:48 normalization >     -- force left join to NULL values (we just need to transfer column types only for the star_intersect macro on schema changes)
2022-04-19 13:15:48 normalization >     left join empty_new_data as inc_data on this_data._airbyte_ab_id = inc_data._airbyte_ab_id
2022-04-19 13:15:48 normalization >     where _airbyte_active_row = 1
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > input_data as (
2022-04-19 13:15:48 normalization >     select "_airbyte_tickets_hashid",
2022-04-19 13:15:48 normalization >   "id",
2022-04-19 13:15:48 normalization >   "url",
2022-04-19 13:15:48 normalization >   "via",
2022-04-19 13:15:48 normalization >   "tags",
2022-04-19 13:15:48 normalization >   "type",
2022-04-19 13:15:48 normalization >   "due_at",
2022-04-19 13:15:48 normalization >   "status",
2022-04-19 13:15:48 normalization >   "subject",
2022-04-19 13:15:48 normalization >   "brand_id",
2022-04-19 13:15:48 normalization >   "group_id",
2022-04-19 13:15:48 normalization >   "priority",
2022-04-19 13:15:48 normalization >   "is_public",
2022-04-19 13:15:48 normalization >   "recipient",
2022-04-19 13:15:48 normalization >   "created_at",
2022-04-19 13:15:48 normalization >   "problem_id",
2022-04-19 13:15:48 normalization >   "updated_at",
2022-04-19 13:15:48 normalization >   "assignee_id",
2022-04-19 13:15:48 normalization >   "description",
2022-04-19 13:15:48 normalization >   "external_id",
2022-04-19 13:15:48 normalization >   "raw_subject",
2022-04-19 13:15:48 normalization >   "email_cc_ids",
2022-04-19 13:15:48 normalization >   "follower_ids",
2022-04-19 13:15:48 normalization >   "followup_ids",
2022-04-19 13:15:48 normalization >   "requester_id",
2022-04-19 13:15:48 normalization >   "submitter_id",
2022-04-19 13:15:48 normalization >   "custom_fields",
2022-04-19 13:15:48 normalization >   "has_incidents",
2022-04-19 13:15:48 normalization >   "forum_topic_id",
2022-04-19 13:15:48 normalization >   "ticket_form_id",
2022-04-19 13:15:48 normalization >   "organization_id",
2022-04-19 13:15:48 normalization >   "collaborator_ids",
2022-04-19 13:15:48 normalization >   "allow_attachments",
2022-04-19 13:15:48 normalization >   "allow_channelback",
2022-04-19 13:15:48 normalization >   "generated_timestamp",
2022-04-19 13:15:48 normalization >   "satisfaction_rating",
2022-04-19 13:15:48 normalization >   "sharing_agreement_ids",
2022-04-19 13:15:48 normalization >   "_airbyte_ab_id",
2022-04-19 13:15:48 normalization >   "_airbyte_emitted_at",
2022-04-19 13:15:48 normalization >   "_airbyte_normalized_at" from new_data
2022-04-19 13:15:48 normalization >     union all
2022-04-19 13:15:48 normalization >     select "_airbyte_tickets_hashid",
2022-04-19 13:15:48 normalization >   "id",
2022-04-19 13:15:48 normalization >   "url",
2022-04-19 13:15:48 normalization >   "via",
2022-04-19 13:15:48 normalization >   "tags",
2022-04-19 13:15:48 normalization >   "type",
2022-04-19 13:15:48 normalization >   "due_at",
2022-04-19 13:15:48 normalization >   "status",
2022-04-19 13:15:48 normalization >   "subject",
2022-04-19 13:15:48 normalization >   "brand_id",
2022-04-19 13:15:48 normalization >   "group_id",
2022-04-19 13:15:48 normalization >   "priority",
2022-04-19 13:15:48 normalization >   "is_public",
2022-04-19 13:15:48 normalization >   "recipient",
2022-04-19 13:15:48 normalization >   "created_at",
2022-04-19 13:15:48 normalization >   "problem_id",
2022-04-19 13:15:48 normalization >   "updated_at",
2022-04-19 13:15:48 normalization >   "assignee_id",
2022-04-19 13:15:48 normalization >   "description",
2022-04-19 13:15:48 normalization >   "external_id",
2022-04-19 13:15:48 normalization >   "raw_subject",
2022-04-19 13:15:48 normalization >   "email_cc_ids",
2022-04-19 13:15:48 normalization >   "follower_ids",
2022-04-19 13:15:48 normalization >   "followup_ids",
2022-04-19 13:15:48 normalization >   "requester_id",
2022-04-19 13:15:48 normalization >   "submitter_id",
2022-04-19 13:15:48 normalization >   "custom_fields",
2022-04-19 13:15:48 normalization >   "has_incidents",
2022-04-19 13:15:48 normalization >   "forum_topic_id",
2022-04-19 13:15:48 normalization >   "ticket_form_id",
2022-04-19 13:15:48 normalization >   "organization_id",
2022-04-19 13:15:48 normalization >   "collaborator_ids",
2022-04-19 13:15:48 normalization >   "allow_attachments",
2022-04-19 13:15:48 normalization >   "allow_channelback",
2022-04-19 13:15:48 normalization >   "generated_timestamp",
2022-04-19 13:15:48 normalization >   "satisfaction_rating",
2022-04-19 13:15:48 normalization >   "sharing_agreement_ids",
2022-04-19 13:15:48 normalization >   "_airbyte_ab_id",
2022-04-19 13:15:48 normalization >   "_airbyte_emitted_at",
2022-04-19 13:15:48 normalization >   "_airbyte_normalized_at" from previous_active_scd_data
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > scd_data as (
2022-04-19 13:15:48 normalization >     -- SQL model to build a Type 2 Slowly Changing Dimension (SCD) table for each record identified by their primary key
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       md5(cast(coalesce(cast(id as varchar), '') as varchar)) as _airbyte_unique_key,
2022-04-19 13:15:48 normalization >       id,
2022-04-19 13:15:48 normalization >       url,
2022-04-19 13:15:48 normalization >       via,
2022-04-19 13:15:48 normalization >       tags,
2022-04-19 13:15:48 normalization >       type,
2022-04-19 13:15:48 normalization >       due_at,
2022-04-19 13:15:48 normalization >       status,
2022-04-19 13:15:48 normalization >       subject,
2022-04-19 13:15:48 normalization >       brand_id,
2022-04-19 13:15:48 normalization >       group_id,
2022-04-19 13:15:48 normalization >       priority,
2022-04-19 13:15:48 normalization >       is_public,
2022-04-19 13:15:48 normalization >       recipient,
2022-04-19 13:15:48 normalization >       created_at,
2022-04-19 13:15:48 normalization >       problem_id,
2022-04-19 13:15:48 normalization >       updated_at,
2022-04-19 13:15:48 normalization >       assignee_id,
2022-04-19 13:15:48 normalization >       description,
2022-04-19 13:15:48 normalization >       external_id,
2022-04-19 13:15:48 normalization >       raw_subject,
2022-04-19 13:15:48 normalization >       email_cc_ids,
2022-04-19 13:15:48 normalization >       follower_ids,
2022-04-19 13:15:48 normalization >       followup_ids,
2022-04-19 13:15:48 normalization >       requester_id,
2022-04-19 13:15:48 normalization >       submitter_id,
2022-04-19 13:15:48 normalization >       custom_fields,
2022-04-19 13:15:48 normalization >       has_incidents,
2022-04-19 13:15:48 normalization >       forum_topic_id,
2022-04-19 13:15:48 normalization >       ticket_form_id,
2022-04-19 13:15:48 normalization >       organization_id,
2022-04-19 13:15:48 normalization >       collaborator_ids,
2022-04-19 13:15:48 normalization >       allow_attachments,
2022-04-19 13:15:48 normalization >       allow_channelback,
2022-04-19 13:15:48 normalization >       generated_timestamp,
2022-04-19 13:15:48 normalization >       satisfaction_rating,
2022-04-19 13:15:48 normalization >       sharing_agreement_ids,
2022-04-19 13:15:48 normalization >       updated_at as _airbyte_start_at,
2022-04-19 13:15:48 normalization >       lag(updated_at) over (
2022-04-19 13:15:48 normalization >         partition by id
2022-04-19 13:15:48 normalization >         order by
2022-04-19 13:15:48 normalization >             updated_at is null asc,
2022-04-19 13:15:48 normalization >             updated_at desc,
2022-04-19 13:15:48 normalization >             _airbyte_emitted_at desc
2022-04-19 13:15:48 normalization >       ) as _airbyte_end_at,
2022-04-19 13:15:48 normalization >       case when row_number() over (
2022-04-19 13:15:48 normalization >         partition by id
2022-04-19 13:15:48 normalization >         order by
2022-04-19 13:15:48 normalization >             updated_at is null asc,
2022-04-19 13:15:48 normalization >             updated_at desc,
2022-04-19 13:15:48 normalization >             _airbyte_emitted_at desc
2022-04-19 13:15:48 normalization >       ) = 1 then 1 else 0 end as _airbyte_active_row,
2022-04-19 13:15:48 normalization >       _airbyte_ab_id,
2022-04-19 13:15:48 normalization >       _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >       _airbyte_tickets_hashid
2022-04-19 13:15:48 normalization >     from input_data
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > dedup_data as (
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >         -- we need to ensure de-duplicated rows for merge/update queries
2022-04-19 13:15:48 normalization >         -- additionally, we generate a unique key for the scd table
2022-04-19 13:15:48 normalization >         row_number() over (
2022-04-19 13:15:48 normalization >             partition by
2022-04-19 13:15:48 normalization >                 _airbyte_unique_key,
2022-04-19 13:15:48 normalization >                 _airbyte_start_at,
2022-04-19 13:15:48 normalization >                 _airbyte_emitted_at
2022-04-19 13:15:48 normalization >             order by _airbyte_active_row desc, _airbyte_ab_id
2022-04-19 13:15:48 normalization >         ) as _airbyte_row_num,
2022-04-19 13:15:48 normalization >         md5(cast(coalesce(cast(_airbyte_unique_key as varchar), '') || '-' || coalesce(cast(_airbyte_start_at as varchar), '') || '-' || coalesce(cast(_airbyte_emitted_at as varchar), '') as varchar)) as _airbyte_unique_key_scd,
2022-04-19 13:15:48 normalization >         scd_data.*
2022-04-19 13:15:48 normalization >     from scd_data
2022-04-19 13:15:48 normalization > )
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     _airbyte_unique_key,
2022-04-19 13:15:48 normalization >     _airbyte_unique_key_scd,
2022-04-19 13:15:48 normalization >     id,
2022-04-19 13:15:48 normalization >     url,
2022-04-19 13:15:48 normalization >     via,
2022-04-19 13:15:48 normalization >     tags,
2022-04-19 13:15:48 normalization >     type,
2022-04-19 13:15:48 normalization >     due_at,
2022-04-19 13:15:48 normalization >     status,
2022-04-19 13:15:48 normalization >     subject,
2022-04-19 13:15:48 normalization >     brand_id,
2022-04-19 13:15:48 normalization >     group_id,
2022-04-19 13:15:48 normalization >     priority,
2022-04-19 13:15:48 normalization >     is_public,
2022-04-19 13:15:48 normalization >     recipient,
2022-04-19 13:15:48 normalization >     created_at,
2022-04-19 13:15:48 normalization >     problem_id,
2022-04-19 13:15:48 normalization >     updated_at,
2022-04-19 13:15:48 normalization >     assignee_id,
2022-04-19 13:15:48 normalization >     description,
2022-04-19 13:15:48 normalization >     external_id,
2022-04-19 13:15:48 normalization >     raw_subject,
2022-04-19 13:15:48 normalization >     email_cc_ids,
2022-04-19 13:15:48 normalization >     follower_ids,
2022-04-19 13:15:48 normalization >     followup_ids,
2022-04-19 13:15:48 normalization >     requester_id,
2022-04-19 13:15:48 normalization >     submitter_id,
2022-04-19 13:15:48 normalization >     custom_fields,
2022-04-19 13:15:48 normalization >     has_incidents,
2022-04-19 13:15:48 normalization >     forum_topic_id,
2022-04-19 13:15:48 normalization >     ticket_form_id,
2022-04-19 13:15:48 normalization >     organization_id,
2022-04-19 13:15:48 normalization >     collaborator_ids,
2022-04-19 13:15:48 normalization >     allow_attachments,
2022-04-19 13:15:48 normalization >     allow_channelback,
2022-04-19 13:15:48 normalization >     generated_timestamp,
2022-04-19 13:15:48 normalization >     satisfaction_rating,
2022-04-19 13:15:48 normalization >     sharing_agreement_ids,
2022-04-19 13:15:48 normalization >     _airbyte_start_at,
2022-04-19 13:15:48 normalization >     _airbyte_end_at,
2022-04-19 13:15:48 normalization >     _airbyte_active_row,
2022-04-19 13:15:48 normalization >     _airbyte_ab_id,
2022-04-19 13:15:48 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:48 normalization >     _airbyte_tickets_hashid
2022-04-19 13:15:48 normalization > from dedup_data where _airbyte_row_num = 1
2022-04-19 13:15:48 normalization >   );
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.007791 (Thread-2): SQL status: INSERT 0 11088 in 0.29 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.013031 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.013245 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_metric_events_scd'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_metric_events_scd'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_metric_events_scd'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.431641 (Thread-2): SQL status: SELECT in 0.42 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.434205 (Thread-2): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.434527 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.434663 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         drop view _airbyte_zendesk.ticket_metric_events_stg
2022-04-19 13:15:48 normalization >       
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.441108 (Thread-2): SQL status: DROP VIEW in 0.01 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.442097 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: COMMIT
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.442247 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.442380 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: COMMIT
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.532852 (Thread-2): SQL status: COMMIT in 0.09 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.533722 (Thread-2): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.533936 (Thread-2): On model.airbyte_utils.ticket_metric_events_scd: Close
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.534906 (Thread-2): 13:15:17 | 8 of 33 OK created incremental model zendesk.ticket_metric_events_scd........................................ [INSERT 0 11088 in 7.48s]
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.535454 (Thread-2): Finished running node model.airbyte_utils.ticket_metric_events_scd
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.535777 (Thread-2): Began running node model.airbyte_utils.sla_policies_filter_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.536577 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_ab1".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.536766 (Thread-2): Compiling model.airbyte_utils.sla_policies_filter_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.558269 (Thread-2): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_ab1"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.558714 (Thread-2): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.559176 (Thread-2): Finished running node model.airbyte_utils.sla_policies_filter_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.559395 (Thread-2): Began running node model.airbyte_utils.sla_policies_policy_metrics_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.560022 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.sla_policies_policy_metrics_ab1".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.560191 (Thread-2): Compiling model.airbyte_utils.sla_policies_policy_metrics_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.589917 (Thread-2): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics_ab1".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.590162 (Thread-2): On model.airbyte_utils.sla_policies_policy_metrics_ab1: BEGIN
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.590318 (Thread-2): Opening a new connection, currently in state closed
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.590435 (Thread-2): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.642443 (Thread-2): SQL status: BEGIN in 0.05 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.642732 (Thread-2): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics_ab1".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.642864 (Thread-2): On model.airbyte_utils.sla_policies_policy_metrics_ab1: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics_ab1"} */
2022-04-19 13:15:48 normalization > with max_value as (
2022-04-19 13:15:48 normalization >             select max(json_array_length(policy_metrics, true)) as max_number_of_items
2022-04-19 13:15:48 normalization >             from "datalake".zendesk."sla_policies"
2022-04-19 13:15:48 normalization >         )
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >             case when max_number_of_items is not null and max_number_of_items > 1
2022-04-19 13:15:48 normalization >             then max_number_of_items
2022-04-19 13:15:48 normalization >             else 1 end as max_number_of_items
2022-04-19 13:15:48 normalization >         from max_value
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.730965 (Thread-2): SQL status: SELECT in 0.09 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.765851 (Thread-2): Writing injected SQL for node "model.airbyte_utils.sla_policies_policy_metrics_ab1"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.766333 (Thread-2): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.766543 (Thread-2): On model.airbyte_utils.sla_policies_policy_metrics_ab1: ROLLBACK
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.776380 (Thread-2): On model.airbyte_utils.sla_policies_policy_metrics_ab1: Close
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.777131 (Thread-2): Finished running node model.airbyte_utils.sla_policies_policy_metrics_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.777374 (Thread-2): Began running node model.airbyte_utils.ticket_metric_events
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.778084 (Thread-2): 13:15:17 | 12 of 33 START incremental model zendesk.ticket_metric_events................................................ [RUN]
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.778730 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.778945 (Thread-2): Compiling model.airbyte_utils.ticket_metric_events
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.806946 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.807193 (Thread-2): On model.airbyte_utils.ticket_metric_events: BEGIN
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.807338 (Thread-2): Opening a new connection, currently in state closed
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.807454 (Thread-2): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.877239 (Thread-2): SQL status: BEGIN in 0.07 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.877544 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.877675 (Thread-2): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_metric_events'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_metric_events'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_metric_events'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.989862 (Thread-3): SQL status: SELECT in 1.46 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.994287 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:17.994534 (Thread-3): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_fields_scd__dbt_tmp131516170906'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_fields_scd__dbt_tmp131516170906'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'None'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_fields_scd__dbt_tmp131516170906'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.403298 (Thread-2): SQL status: SELECT in 0.53 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.406211 (Thread-2): Writing injected SQL for node "model.airbyte_utils.ticket_metric_events"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.406578 (Thread-2): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.421633 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.421846 (Thread-3): SQL status: SELECT in 0.43 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.422024 (Thread-2): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_metric_events'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_metric_events'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_metric_events'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.436812 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.437223 (Thread-3): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_fields_scd'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_fields_scd'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_fields_scd'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.714339 (Thread-2): SQL status: SELECT in 0.28 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.719873 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.720065 (Thread-2): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >   create temporary table
2022-04-19 13:15:48 normalization >     "ticket_metric_events__dbt_tmp131518411171"
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >       compound sortkey(_airbyte_unique_key,_airbyte_emitted_at)
2022-04-19 13:15:48 normalization >   as (
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > -- Final base SQL model
2022-04-19 13:15:48 normalization > -- depends_on: "datalake".zendesk."ticket_metric_events_scd"
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     _airbyte_unique_key,
2022-04-19 13:15:48 normalization >     id,
2022-04-19 13:15:48 normalization >     "time",
2022-04-19 13:15:48 normalization >     type,
2022-04-19 13:15:48 normalization >     metric,
2022-04-19 13:15:48 normalization >     ticket_id,
2022-04-19 13:15:48 normalization >     instance_id,
2022-04-19 13:15:48 normalization >     _airbyte_ab_id,
2022-04-19 13:15:48 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:48 normalization >     _airbyte_ticket_metric_events_hashid
2022-04-19 13:15:48 normalization > from "datalake".zendesk."ticket_metric_events_scd"
2022-04-19 13:15:48 normalization > -- ticket_metric_events from "datalake".zendesk._airbyte_raw_ticket_metric_events
2022-04-19 13:15:48 normalization > where 1 = 1
2022-04-19 13:15:48 normalization > and _airbyte_active_row = 1
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > and coalesce(
2022-04-19 13:15:48 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:48 normalization >     timestamp with time zone
2022-04-19 13:15:48 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:48 normalization >     timestamp with time zone
2022-04-19 13:15:48 normalization > )) from "datalake".zendesk."ticket_metric_events"),
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     true)
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >   );
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.755053 (Thread-3): SQL status: SELECT in 0.32 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.761275 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.761470 (Thread-3): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_fields_scd__dbt_tmp131516170906'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_fields_scd__dbt_tmp131516170906'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'None'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_fields_scd__dbt_tmp131516170906'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.920163 (Thread-1): SQL status: SELECT in 2.02 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.925026 (Thread-1): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.925236 (Thread-1): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'tickets_scd__dbt_tmp131516397949'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'tickets_scd__dbt_tmp131516397949'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'None'
2022-04-19 13:15:48 normalization >         and tablename = 'tickets_scd__dbt_tmp131516397949'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.984381 (Thread-3): SQL status: SELECT in 0.22 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.989884 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:18.990066 (Thread-3): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_fields_scd'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_fields_scd'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_fields_scd'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.149354 (Thread-1): SQL status: SELECT in 0.22 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.156311 (Thread-1): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.156499 (Thread-1): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'tickets_scd'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'tickets_scd'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'tickets_scd'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.212340 (Thread-3): SQL status: SELECT in 0.22 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.221324 (Thread-3): 
2022-04-19 13:15:48 normalization >     In "datalake"."zendesk"."ticket_fields_scd":
2022-04-19 13:15:48 normalization >         Schema changed: False
2022-04-19 13:15:48 normalization >         Source columns not in target: []
2022-04-19 13:15:48 normalization >         Target columns not in source: []
2022-04-19 13:15:48 normalization >         New column types: []
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.224367 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.224522 (Thread-3): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_fields_scd'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_fields_scd'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_fields_scd'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.370883 (Thread-1): SQL status: SELECT in 0.21 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.379492 (Thread-1): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.379671 (Thread-1): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'tickets_scd__dbt_tmp131516397949'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'tickets_scd__dbt_tmp131516397949'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'None'
2022-04-19 13:15:48 normalization >         and tablename = 'tickets_scd__dbt_tmp131516397949'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.443551 (Thread-3): SQL status: SELECT in 0.22 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.446809 (Thread-3): Writing runtime SQL for node "model.airbyte_utils.ticket_fields_scd"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.447266 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.447424 (Thread-3): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       delete
2022-04-19 13:15:48 normalization >     from "datalake".zendesk."ticket_fields_scd"
2022-04-19 13:15:48 normalization >     where (_airbyte_unique_key_scd) in (
2022-04-19 13:15:48 normalization >         select (_airbyte_unique_key_scd)
2022-04-19 13:15:48 normalization >         from "ticket_fields_scd__dbt_tmp131516170906"
2022-04-19 13:15:48 normalization >     );
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     insert into "datalake".zendesk."ticket_fields_scd" ("_airbyte_unique_key", "_airbyte_unique_key_scd", "id", "tag", "url", "type", "title", "active", "position", "required", "raw_title", "removable", "created_at", "updated_at", "description", "sub_type_id", "raw_description", "title_in_portal", "agent_description", "visible_in_portal", "editable_in_portal", "required_in_portal", "raw_title_in_portal", "collapsed_for_agents", "custom_field_options", "system_field_options", "regexp_for_validation", "_airbyte_start_at", "_airbyte_end_at", "_airbyte_active_row", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_fields_hashid")
2022-04-19 13:15:48 normalization >     (
2022-04-19 13:15:48 normalization >        select "_airbyte_unique_key", "_airbyte_unique_key_scd", "id", "tag", "url", "type", "title", "active", "position", "required", "raw_title", "removable", "created_at", "updated_at", "description", "sub_type_id", "raw_description", "title_in_portal", "agent_description", "visible_in_portal", "editable_in_portal", "required_in_portal", "raw_title_in_portal", "collapsed_for_agents", "custom_field_options", "system_field_options", "regexp_for_validation", "_airbyte_start_at", "_airbyte_end_at", "_airbyte_active_row", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_fields_hashid"
2022-04-19 13:15:48 normalization >        from "ticket_fields_scd__dbt_tmp131516170906"
2022-04-19 13:15:48 normalization >     );
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.617022 (Thread-1): SQL status: SELECT in 0.24 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.623064 (Thread-1): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.623226 (Thread-1): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'tickets_scd'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'tickets_scd'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'tickets_scd'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.697094 (Thread-2): SQL status: SELECT in 0.98 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.701550 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.701711 (Thread-2): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_metric_events__dbt_tmp131518411171'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_metric_events__dbt_tmp131518411171'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'None'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_metric_events__dbt_tmp131518411171'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.859935 (Thread-1): SQL status: SELECT in 0.24 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.873618 (Thread-1): 
2022-04-19 13:15:48 normalization >     In "datalake"."zendesk"."tickets_scd":
2022-04-19 13:15:48 normalization >         Schema changed: True
2022-04-19 13:15:48 normalization >         Source columns not in target: []
2022-04-19 13:15:48 normalization >         Target columns not in source: []
2022-04-19 13:15:48 normalization >         New column types: [{'column_name': '_airbyte_start_at', 'new_type': 'timestamp with time zone'}, {'column_name': '_airbyte_end_at', 'new_type': 'timestamp with time zone'}]
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.891344 (Thread-1): Using redshift connection "model.airbyte_utils.tickets_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.891560 (Thread-1): On model.airbyte_utils.tickets_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.tickets_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     alter table "datalake"."zendesk"."tickets_scd" add column "_airbyte_start_at__dbt_alter" timestamp with time zone;
2022-04-19 13:15:48 normalization >     update "datalake"."zendesk"."tickets_scd" set "_airbyte_start_at__dbt_alter" = "_airbyte_start_at";
2022-04-19 13:15:48 normalization >     alter table "datalake"."zendesk"."tickets_scd" drop column "_airbyte_start_at" cascade;
2022-04-19 13:15:48 normalization >     alter table "datalake"."zendesk"."tickets_scd" rename column "_airbyte_start_at__dbt_alter" to "_airbyte_start_at"
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.904420 (Thread-2): SQL status: SELECT in 0.20 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.909437 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.909618 (Thread-2): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_metric_events'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_metric_events'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_metric_events'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.995509 (Thread-1): Postgres error: column "_airbyte_start_at__dbt_alter" is of type timestamp with time zone but expression is of type bigint
2022-04-19 13:15:48 normalization > HINT:  You will need to rewrite or cast the expression.
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.995830 (Thread-1): On model.airbyte_utils.tickets_scd: ROLLBACK
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.999523 (Thread-1): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:19.999752 (Thread-1): On model.airbyte_utils.tickets_scd: Close
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.000488 (Thread-1): Database Error in model tickets_scd (models/generated/airbyte_incremental/scd/zendesk/tickets_scd.sql)
2022-04-19 13:15:48 normalization >   column "_airbyte_start_at__dbt_alter" is of type timestamp with time zone but expression is of type bigint
2022-04-19 13:15:48 normalization >   HINT:  You will need to rewrite or cast the expression.
2022-04-19 13:15:48 normalization > Traceback (most recent call last):
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/adapters/postgres/connections.py", line 56, in exception_handler
2022-04-19 13:15:48 normalization >     yield
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/adapters/sql/connections.py", line 80, in add_query
2022-04-19 13:15:48 normalization >     cursor.execute(sql, bindings)
2022-04-19 13:15:48 normalization > psycopg2.errors.DatatypeMismatch: column "_airbyte_start_at__dbt_alter" is of type timestamp with time zone but expression is of type bigint
2022-04-19 13:15:48 normalization > HINT:  You will need to rewrite or cast the expression.
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > The above exception was the direct cause of the following exception:
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > Traceback (most recent call last):
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/task/base.py", line 348, in safe_run
2022-04-19 13:15:48 normalization >     result = self.compile_and_execute(manifest, ctx)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/task/base.py", line 291, in compile_and_execute
2022-04-19 13:15:48 normalization >     result = self.run(ctx.node, manifest)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/task/base.py", line 393, in run
2022-04-19 13:15:48 normalization >     return self.execute(compiled_node, manifest)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/task/run.py", line 249, in execute
2022-04-19 13:15:48 normalization >     result = MacroGenerator(materialization_macro, context)()
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 333, in __call__
2022-04-19 13:15:48 normalization >     return self.call_macro(*args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 260, in call_macro
2022-04-19 13:15:48 normalization >     return macro(*args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 675, in __call__
2022-04-19 13:15:48 normalization >     return self._invoke(arguments, autoescape)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 679, in _invoke
2022-04-19 13:15:48 normalization >     rv = self._func(*arguments)
2022-04-19 13:15:48 normalization >   File "<template>", line 148, in macro
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/sandbox.py", line 462, in call
2022-04-19 13:15:48 normalization >     return __context.call(__obj, *args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 290, in call
2022-04-19 13:15:48 normalization >     return __obj(*args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 333, in __call__
2022-04-19 13:15:48 normalization >     return self.call_macro(*args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 260, in call_macro
2022-04-19 13:15:48 normalization >     return macro(*args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 675, in __call__
2022-04-19 13:15:48 normalization >     return self._invoke(arguments, autoescape)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 679, in _invoke
2022-04-19 13:15:48 normalization >     rv = self._func(*arguments)
2022-04-19 13:15:48 normalization >   File "<template>", line 66, in macro
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/sandbox.py", line 462, in call
2022-04-19 13:15:48 normalization >     return __context.call(__obj, *args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 290, in call
2022-04-19 13:15:48 normalization >     return __obj(*args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 333, in __call__
2022-04-19 13:15:48 normalization >     return self.call_macro(*args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 260, in call_macro
2022-04-19 13:15:48 normalization >     return macro(*args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 675, in __call__
2022-04-19 13:15:48 normalization >     return self._invoke(arguments, autoescape)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 679, in _invoke
2022-04-19 13:15:48 normalization >     rv = self._func(*arguments)
2022-04-19 13:15:48 normalization >   File "<template>", line 63, in macro
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/sandbox.py", line 462, in call
2022-04-19 13:15:48 normalization >     return __context.call(__obj, *args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 290, in call
2022-04-19 13:15:48 normalization >     return __obj(*args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 333, in __call__
2022-04-19 13:15:48 normalization >     return self.call_macro(*args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 260, in call_macro
2022-04-19 13:15:48 normalization >     return macro(*args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 675, in __call__
2022-04-19 13:15:48 normalization >     return self._invoke(arguments, autoescape)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 679, in _invoke
2022-04-19 13:15:48 normalization >     rv = self._func(*arguments)
2022-04-19 13:15:48 normalization >   File "<template>", line 24, in macro
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/sandbox.py", line 462, in call
2022-04-19 13:15:48 normalization >     return __context.call(__obj, *args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 290, in call
2022-04-19 13:15:48 normalization >     return __obj(*args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 333, in __call__
2022-04-19 13:15:48 normalization >     return self.call_macro(*args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 260, in call_macro
2022-04-19 13:15:48 normalization >     return macro(*args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 675, in __call__
2022-04-19 13:15:48 normalization >     return self._invoke(arguments, autoescape)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 679, in _invoke
2022-04-19 13:15:48 normalization >     rv = self._func(*arguments)
2022-04-19 13:15:48 normalization >   File "<template>", line 55, in macro
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/sandbox.py", line 462, in call
2022-04-19 13:15:48 normalization >     return __context.call(__obj, *args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 290, in call
2022-04-19 13:15:48 normalization >     return __obj(*args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 333, in __call__
2022-04-19 13:15:48 normalization >     return self.call_macro(*args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/clients/jinja.py", line 260, in call_macro
2022-04-19 13:15:48 normalization >     return macro(*args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 675, in __call__
2022-04-19 13:15:48 normalization >     return self._invoke(arguments, autoescape)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 679, in _invoke
2022-04-19 13:15:48 normalization >     rv = self._func(*arguments)
2022-04-19 13:15:48 normalization >   File "<template>", line 41, in macro
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/sandbox.py", line 462, in call
2022-04-19 13:15:48 normalization >     return __context.call(__obj, *args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/jinja2/runtime.py", line 290, in call
2022-04-19 13:15:48 normalization >     return __obj(*args, **kwargs)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/adapters/base/impl.py", line 226, in execute
2022-04-19 13:15:48 normalization >     return self.connections.execute(
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/adapters/sql/connections.py", line 131, in execute
2022-04-19 13:15:48 normalization >     _, cursor = self.add_query(sql, auto_begin)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/adapters/sql/connections.py", line 87, in add_query
2022-04-19 13:15:48 normalization >     return connection, cursor
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/contextlib.py", line 131, in __exit__
2022-04-19 13:15:48 normalization >     self.gen.throw(type, value, traceback)
2022-04-19 13:15:48 normalization >   File "/usr/local/lib/python3.8/site-packages/dbt/adapters/postgres/connections.py", line 67, in exception_handler
2022-04-19 13:15:48 normalization >     raise dbt.exceptions.DatabaseException(str(e).strip()) from e
2022-04-19 13:15:48 normalization > dbt.exceptions.DatabaseException: Database Error in model tickets_scd (models/generated/airbyte_incremental/scd/zendesk/tickets_scd.sql)
2022-04-19 13:15:48 normalization >   column "_airbyte_start_at__dbt_alter" is of type timestamp with time zone but expression is of type bigint
2022-04-19 13:15:48 normalization >   HINT:  You will need to rewrite or cast the expression.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.010412 (Thread-1): 13:15:20 | 11 of 33 ERROR creating incremental model zendesk.tickets_scd................................................ [ERROR in 6.47s]
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.010692 (Thread-1): Finished running node model.airbyte_utils.tickets_scd
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.011097 (Thread-1): Began running node model.airbyte_utils.sla_policies_filter_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.011941 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_ab2".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.012348 (Thread-1): Compiling model.airbyte_utils.sla_policies_filter_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.023154 (Thread-1): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_ab2"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.023496 (Thread-1): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.023901 (Thread-1): Finished running node model.airbyte_utils.sla_policies_filter_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.024108 (Thread-1): Began running node model.airbyte_utils.sla_policies_policy_metrics_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.024490 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.sla_policies_policy_metrics_ab2".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.024818 (Thread-1): Compiling model.airbyte_utils.sla_policies_policy_metrics_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.039589 (Thread-1): Writing injected SQL for node "model.airbyte_utils.sla_policies_policy_metrics_ab2"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.039913 (Thread-1): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.040349 (Thread-1): Finished running node model.airbyte_utils.sla_policies_policy_metrics_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.040556 (Thread-1): Began running node model.airbyte_utils.tickets
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.040764 (Thread-1): 13:15:20 | 13 of 33 SKIP relation zendesk.tickets....................................................................... [SKIP]
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.041071 (Thread-1): Finished running node model.airbyte_utils.tickets
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.041307 (Thread-1): Began running node model.airbyte_utils.tickets_custom_fields_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.041478 (Thread-1): Finished running node model.airbyte_utils.tickets_custom_fields_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.041643 (Thread-1): Began running node model.airbyte_utils.tickets_satisfaction_rating_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.041811 (Thread-1): Finished running node model.airbyte_utils.tickets_satisfaction_rating_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.041979 (Thread-1): Began running node model.airbyte_utils.tickets_via_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.042496 (Thread-1): Finished running node model.airbyte_utils.tickets_via_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.042700 (Thread-1): Began running node model.airbyte_utils.sla_policies_filter_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.043299 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_ab3".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.043494 (Thread-1): Compiling model.airbyte_utils.sla_policies_filter_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.060918 (Thread-1): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_ab3"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.061302 (Thread-1): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.061744 (Thread-1): Finished running node model.airbyte_utils.sla_policies_filter_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.061952 (Thread-1): Began running node model.airbyte_utils.tickets_custom_fields_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.062344 (Thread-1): Finished running node model.airbyte_utils.tickets_custom_fields_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.062535 (Thread-1): Began running node model.airbyte_utils.tickets_satisfaction_rating_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.063010 (Thread-1): Finished running node model.airbyte_utils.tickets_satisfaction_rating_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.063185 (Thread-1): Began running node model.airbyte_utils.tickets_via_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.063491 (Thread-1): Finished running node model.airbyte_utils.tickets_via_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.063818 (Thread-1): Began running node model.airbyte_utils.sla_policies_policy_metrics_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.064311 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.sla_policies_policy_metrics_ab3".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.064640 (Thread-1): Compiling model.airbyte_utils.sla_policies_policy_metrics_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.077055 (Thread-2): SQL status: SELECT in 0.17 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.083661 (Thread-1): Writing injected SQL for node "model.airbyte_utils.sla_policies_policy_metrics_ab3"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.088316 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.088664 (Thread-2): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_metric_events__dbt_tmp131518411171'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_metric_events__dbt_tmp131518411171'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'None'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_metric_events__dbt_tmp131518411171'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.088987 (Thread-1): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.089445 (Thread-1): Finished running node model.airbyte_utils.sla_policies_policy_metrics_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.089656 (Thread-1): Began running node model.airbyte_utils.tickets_custom_fields_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.089859 (Thread-1): Finished running node model.airbyte_utils.tickets_custom_fields_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.090208 (Thread-1): Began running node model.airbyte_utils.sla_policies_filter
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.090550 (Thread-1): 13:15:20 | 14 of 33 START incremental model zendesk.sla_policies_filter................................................. [RUN]
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.091799 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.091981 (Thread-1): Compiling model.airbyte_utils.sla_policies_filter
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.102498 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.102720 (Thread-3): SQL status: INSERT 0 28 in 0.66 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.102911 (Thread-1): On model.airbyte_utils.sla_policies_filter: BEGIN
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.106981 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.107223 (Thread-1): Opening a new connection, currently in state closed
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.109039 (Thread-3): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_fields_scd'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_fields_scd'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_fields_scd'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.109260 (Thread-1): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.153767 (Thread-1): SQL status: BEGIN in 0.05 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.154059 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.154202 (Thread-1): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_filter'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_filter'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_filter'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.401773 (Thread-2): SQL status: SELECT in 0.31 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.406768 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.406952 (Thread-2): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_metric_events'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_metric_events'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_metric_events'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.501015 (Thread-3): SQL status: SELECT in 0.39 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.504426 (Thread-3): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.504759 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.504902 (Thread-3): On model.airbyte_utils.ticket_fields_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_scd"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         drop view _airbyte_zendesk.ticket_fields_stg
2022-04-19 13:15:48 normalization >       
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.515042 (Thread-3): SQL status: DROP VIEW in 0.01 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.515900 (Thread-3): On model.airbyte_utils.ticket_fields_scd: COMMIT
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.516046 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_fields_scd".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.516166 (Thread-3): On model.airbyte_utils.ticket_fields_scd: COMMIT
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.585609 (Thread-1): SQL status: SELECT in 0.43 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.596291 (Thread-1): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.596692 (Thread-1): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.604701 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.604869 (Thread-1): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_filter'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_filter'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_filter'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.620399 (Thread-3): SQL status: COMMIT in 0.10 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.621222 (Thread-3): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.621431 (Thread-3): On model.airbyte_utils.ticket_fields_scd: Close
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.622251 (Thread-3): 13:15:20 | 9 of 33 OK created incremental model zendesk.ticket_fields_scd............................................... [INSERT 0 28 in 7.84s]
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.622756 (Thread-3): Finished running node model.airbyte_utils.ticket_fields_scd
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.623233 (Thread-3): Began running node model.airbyte_utils.tickets_satisfaction_rating_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.623440 (Thread-3): Finished running node model.airbyte_utils.tickets_satisfaction_rating_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.623623 (Thread-3): Began running node model.airbyte_utils.tickets_via_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.624076 (Thread-3): Finished running node model.airbyte_utils.tickets_via_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.624308 (Thread-3): Began running node model.airbyte_utils.sla_policies_policy_metrics
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.624916 (Thread-3): 13:15:20 | 15 of 33 START incremental model zendesk.sla_policies_policy_metrics......................................... [RUN]
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.625697 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.625878 (Thread-3): Compiling model.airbyte_utils.sla_policies_policy_metrics
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.636067 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.636238 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: BEGIN
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.636453 (Thread-3): Opening a new connection, currently in state closed
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.636570 (Thread-3): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.692837 (Thread-3): SQL status: BEGIN in 0.06 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.693134 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.693266 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_policy_metrics'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_policy_metrics'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_policy_metrics'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.701034 (Thread-2): SQL status: SELECT in 0.29 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.704675 (Thread-2): 
2022-04-19 13:15:48 normalization >     In "datalake"."zendesk"."ticket_metric_events":
2022-04-19 13:15:48 normalization >         Schema changed: True
2022-04-19 13:15:48 normalization >         Source columns not in target: [<Column _airbyte_unique_key (character varying(32))>]
2022-04-19 13:15:48 normalization >         Target columns not in source: []
2022-04-19 13:15:48 normalization >         New column types: []
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.713172 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.713333 (Thread-2): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     alter table "datalake"."zendesk"."ticket_metric_events" add column _airbyte_unique_key character varying(32)
2022-04-19 13:15:48 normalization >       
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.728369 (Thread-2): SQL status: ALTER TABLE in 0.01 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.729758 (Thread-2): 
2022-04-19 13:15:48 normalization >     In "datalake"."zendesk"."ticket_metric_events":
2022-04-19 13:15:48 normalization >         Schema change approach: sync_all_columns
2022-04-19 13:15:48 normalization >         Columns added: [<Column _airbyte_unique_key (character varying(32))>]
2022-04-19 13:15:48 normalization >         Columns removed: []
2022-04-19 13:15:48 normalization >         Data types changed: []
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.734736 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.734894 (Thread-2): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_metric_events'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_metric_events'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_metric_events'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.983314 (Thread-1): SQL status: SELECT in 0.38 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.988867 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:20.989065 (Thread-1): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >   create temporary table
2022-04-19 13:15:48 normalization >     "sla_policies_filter__dbt_tmp131520601550"
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >       compound sortkey(_airbyte_emitted_at)
2022-04-19 13:15:48 normalization >   as (
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > with __dbt__cte__sla_policies_filter_ab1 as (
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:48 normalization > -- depends_on: "datalake".zendesk."sla_policies"
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     _airbyte_sla_policies_hashid,
2022-04-19 13:15:48 normalization >     json_extract_path_text(filter, 'all', true) as "all",
2022-04-19 13:15:48 normalization >     json_extract_path_text(filter, 'any', true) as "any",
2022-04-19 13:15:48 normalization >     _airbyte_ab_id,
2022-04-19 13:15:48 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:48 normalization > from "datalake".zendesk."sla_policies" as table_alias
2022-04-19 13:15:48 normalization > -- filter at sla_policies/filter
2022-04-19 13:15:48 normalization > where 1 = 1
2022-04-19 13:15:48 normalization > and filter is not null
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > ),  __dbt__cte__sla_policies_filter_ab2 as (
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:48 normalization > -- depends_on: __dbt__cte__sla_policies_filter_ab1
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     _airbyte_sla_policies_hashid,
2022-04-19 13:15:48 normalization >     "all",
2022-04-19 13:15:48 normalization >     "any",
2022-04-19 13:15:48 normalization >     _airbyte_ab_id,
2022-04-19 13:15:48 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:48 normalization > from __dbt__cte__sla_policies_filter_ab1
2022-04-19 13:15:48 normalization > -- filter at sla_policies/filter
2022-04-19 13:15:48 normalization > where 1 = 1
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > ),  __dbt__cte__sla_policies_filter_ab3 as (
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > -- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:48 normalization > -- depends_on: __dbt__cte__sla_policies_filter_ab2
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     md5(cast(coalesce(cast(_airbyte_sla_policies_hashid as varchar), '') || '-' || coalesce(cast("all" as varchar), '') || '-' || coalesce(cast("any" as varchar), '') as varchar)) as _airbyte_filter_hashid,
2022-04-19 13:15:48 normalization >     tmp.*
2022-04-19 13:15:48 normalization > from __dbt__cte__sla_policies_filter_ab2 tmp
2022-04-19 13:15:48 normalization > -- filter at sla_policies/filter
2022-04-19 13:15:48 normalization > where 1 = 1
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > )-- Final base SQL model
2022-04-19 13:15:48 normalization > -- depends_on: __dbt__cte__sla_policies_filter_ab3
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     _airbyte_sla_policies_hashid,
2022-04-19 13:15:48 normalization >     "all",
2022-04-19 13:15:48 normalization >     "any",
2022-04-19 13:15:48 normalization >     _airbyte_ab_id,
2022-04-19 13:15:48 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:48 normalization >     _airbyte_filter_hashid
2022-04-19 13:15:48 normalization > from __dbt__cte__sla_policies_filter_ab3
2022-04-19 13:15:48 normalization > -- filter at sla_policies/filter from "datalake".zendesk."sla_policies"
2022-04-19 13:15:48 normalization > where 1 = 1
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > and coalesce(
2022-04-19 13:15:48 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:48 normalization >     timestamp with time zone
2022-04-19 13:15:48 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:48 normalization >     timestamp with time zone
2022-04-19 13:15:48 normalization > )) from "datalake".zendesk."sla_policies_filter"),
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     true)
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >   );
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.068945 (Thread-2): SQL status: SELECT in 0.33 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.071483 (Thread-2): Writing runtime SQL for node "model.airbyte_utils.ticket_metric_events"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.071887 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.072028 (Thread-2): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       delete
2022-04-19 13:15:48 normalization >     from "datalake".zendesk."ticket_metric_events"
2022-04-19 13:15:48 normalization >     where (_airbyte_unique_key) in (
2022-04-19 13:15:48 normalization >         select (_airbyte_unique_key)
2022-04-19 13:15:48 normalization >         from "ticket_metric_events__dbt_tmp131518411171"
2022-04-19 13:15:48 normalization >     );
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     insert into "datalake".zendesk."ticket_metric_events" ("id", "time", "type", "metric", "ticket_id", "instance_id", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_metric_events_hashid", "_airbyte_unique_key")
2022-04-19 13:15:48 normalization >     (
2022-04-19 13:15:48 normalization >        select "id", "time", "type", "metric", "ticket_id", "instance_id", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_metric_events_hashid", "_airbyte_unique_key"
2022-04-19 13:15:48 normalization >        from "ticket_metric_events__dbt_tmp131518411171"
2022-04-19 13:15:48 normalization >     );
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.086790 (Thread-3): SQL status: SELECT in 0.39 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.097855 (Thread-3): Writing injected SQL for node "model.airbyte_utils.sla_policies_policy_metrics"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.098229 (Thread-3): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.106552 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.106723 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_policy_metrics'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_policy_metrics'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_policy_metrics'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.192213 (Thread-1): SQL status: SELECT in 0.20 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.196508 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.196666 (Thread-1): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_filter__dbt_tmp131520601550'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_filter__dbt_tmp131520601550'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'None'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_filter__dbt_tmp131520601550'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.364711 (Thread-3): SQL status: SELECT in 0.26 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.370447 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.370613 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >   create temporary table
2022-04-19 13:15:48 normalization >     "sla_policies_policy_metrics__dbt_tmp131521102959"
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >       compound sortkey(_airbyte_emitted_at)
2022-04-19 13:15:48 normalization >   as (
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > with __dbt__cte__sla_policies_policy_metrics_ab1 as (
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:48 normalization > -- depends_on: "datalake".zendesk."sla_policies"
2022-04-19 13:15:48 normalization > with numbers as (
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     with p as (
2022-04-19 13:15:48 normalization >         select 0 as generated_number union all select 1
2022-04-19 13:15:48 normalization >     ), unioned as (
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p0.generated_number * power(2, 0)
2022-04-19 13:15:48 normalization >      + 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p1.generated_number * power(2, 1)
2022-04-19 13:15:48 normalization >      + 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p2.generated_number * power(2, 2)
2022-04-19 13:15:48 normalization >      + 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p3.generated_number * power(2, 3)
2022-04-19 13:15:48 normalization >      + 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p4.generated_number * power(2, 4)
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     + 1
2022-04-19 13:15:48 normalization >     as generated_number
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p as p0
2022-04-19 13:15:48 normalization >      cross join 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p as p1
2022-04-19 13:15:48 normalization >      cross join 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p as p2
2022-04-19 13:15:48 normalization >      cross join 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p as p3
2022-04-19 13:15:48 normalization >      cross join 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p as p4
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select *
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     where generated_number <= 24
2022-04-19 13:15:48 normalization >     order by generated_number
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > joined as (
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >         _airbyte_sla_policies_hashid as _airbyte_hashid,
2022-04-19 13:15:48 normalization >         json_extract_array_element_text(policy_metrics, numbers.generated_number::int - 1, true) as _airbyte_nested_data
2022-04-19 13:15:48 normalization >     from "datalake".zendesk."sla_policies"
2022-04-19 13:15:48 normalization >     cross join numbers
2022-04-19 13:15:48 normalization >     -- only generate the number of records in the cross join that corresponds
2022-04-19 13:15:48 normalization >     -- to the number of items in "datalake".zendesk."sla_policies".policy_metrics
2022-04-19 13:15:48 normalization >     where numbers.generated_number <= json_array_length(policy_metrics, true)
2022-04-19 13:15:48 normalization > )
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     _airbyte_sla_policies_hashid,
2022-04-19 13:15:48 normalization >     case when json_extract_path_text(_airbyte_nested_data, 'metric', true) != '' then json_extract_path_text(_airbyte_nested_data, 'metric', true) end as metric,
2022-04-19 13:15:48 normalization >     case when json_extract_path_text(_airbyte_nested_data, 'target', true) != '' then json_extract_path_text(_airbyte_nested_data, 'target', true) end as target,
2022-04-19 13:15:48 normalization >     case when json_extract_path_text(_airbyte_nested_data, 'priority', true) != '' then json_extract_path_text(_airbyte_nested_data, 'priority', true) end as priority,
2022-04-19 13:15:48 normalization >     case when json_extract_path_text(_airbyte_nested_data, 'business_hours', true) != '' then json_extract_path_text(_airbyte_nested_data, 'business_hours', true) end as business_hours,
2022-04-19 13:15:48 normalization >     _airbyte_ab_id,
2022-04-19 13:15:48 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:48 normalization > from "datalake".zendesk."sla_policies" as table_alias
2022-04-19 13:15:48 normalization > -- policy_metrics at sla_policies/policy_metrics
2022-04-19 13:15:48 normalization > left join joined on _airbyte_sla_policies_hashid = joined._airbyte_hashid
2022-04-19 13:15:48 normalization > where 1 = 1
2022-04-19 13:15:48 normalization > and policy_metrics is not null
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > ),  __dbt__cte__sla_policies_policy_metrics_ab2 as (
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:48 normalization > -- depends_on: __dbt__cte__sla_policies_policy_metrics_ab1
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     _airbyte_sla_policies_hashid,
2022-04-19 13:15:48 normalization >     cast(metric as varchar) as metric,
2022-04-19 13:15:48 normalization >     cast(target as 
2022-04-19 13:15:48 normalization >     bigint
2022-04-19 13:15:48 normalization > ) as target,
2022-04-19 13:15:48 normalization >     cast(priority as varchar) as priority,
2022-04-19 13:15:48 normalization >     cast(decode(business_hours, 'true', '1', 'false', '0')::integer as boolean) as business_hours,
2022-04-19 13:15:48 normalization >     _airbyte_ab_id,
2022-04-19 13:15:48 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:48 normalization > from __dbt__cte__sla_policies_policy_metrics_ab1
2022-04-19 13:15:48 normalization > -- policy_metrics at sla_policies/policy_metrics
2022-04-19 13:15:48 normalization > where 1 = 1
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > ),  __dbt__cte__sla_policies_policy_metrics_ab3 as (
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > -- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:48 normalization > -- depends_on: __dbt__cte__sla_policies_policy_metrics_ab2
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     md5(cast(coalesce(cast(_airbyte_sla_policies_hashid as varchar), '') || '-' || coalesce(cast(metric as varchar), '') || '-' || coalesce(cast(target as varchar), '') || '-' || coalesce(cast(priority as varchar), '') || '-' || coalesce(cast(case when business_hours then 'true' else 'false' end as varchar), '') as varchar)) as _airbyte_policy_metrics_hashid,
2022-04-19 13:15:48 normalization >     tmp.*
2022-04-19 13:15:48 normalization > from __dbt__cte__sla_policies_policy_metrics_ab2 tmp
2022-04-19 13:15:48 normalization > -- policy_metrics at sla_policies/policy_metrics
2022-04-19 13:15:48 normalization > where 1 = 1
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > )-- Final base SQL model
2022-04-19 13:15:48 normalization > -- depends_on: __dbt__cte__sla_policies_policy_metrics_ab3
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     _airbyte_sla_policies_hashid,
2022-04-19 13:15:48 normalization >     metric,
2022-04-19 13:15:48 normalization >     target,
2022-04-19 13:15:48 normalization >     priority,
2022-04-19 13:15:48 normalization >     business_hours,
2022-04-19 13:15:48 normalization >     _airbyte_ab_id,
2022-04-19 13:15:48 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:48 normalization >     _airbyte_policy_metrics_hashid
2022-04-19 13:15:48 normalization > from __dbt__cte__sla_policies_policy_metrics_ab3
2022-04-19 13:15:48 normalization > -- policy_metrics at sla_policies/policy_metrics from "datalake".zendesk."sla_policies"
2022-04-19 13:15:48 normalization > where 1 = 1
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > and coalesce(
2022-04-19 13:15:48 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:48 normalization >     timestamp with time zone
2022-04-19 13:15:48 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:48 normalization >     timestamp with time zone
2022-04-19 13:15:48 normalization > )) from "datalake".zendesk."sla_policies_policy_metrics"),
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     true)
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >   );
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.444054 (Thread-1): SQL status: SELECT in 0.25 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.449224 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.449384 (Thread-1): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_filter'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_filter'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_filter'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.686540 (Thread-1): SQL status: SELECT in 0.24 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.691897 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.692068 (Thread-1): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_filter__dbt_tmp131520601550'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_filter__dbt_tmp131520601550'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'None'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_filter__dbt_tmp131520601550'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.827195 (Thread-1): SQL status: SELECT in 0.13 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.832210 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.832407 (Thread-1): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_filter'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_filter'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_filter'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.994753 (Thread-1): SQL status: SELECT in 0.16 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:21.997721 (Thread-1): 
2022-04-19 13:15:48 normalization >     In "datalake"."zendesk"."sla_policies_filter":
2022-04-19 13:15:48 normalization >         Schema changed: False
2022-04-19 13:15:48 normalization >         Source columns not in target: []
2022-04-19 13:15:48 normalization >         Target columns not in source: []
2022-04-19 13:15:48 normalization >         New column types: []
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.002704 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.002859 (Thread-1): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_filter'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_filter'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_filter'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.070759 (Thread-3): SQL status: SELECT in 0.70 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.075364 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.075532 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_policy_metrics__dbt_tmp131521102959'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_policy_metrics__dbt_tmp131521102959'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'None'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_policy_metrics__dbt_tmp131521102959'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.193272 (Thread-1): SQL status: SELECT in 0.19 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.195605 (Thread-1): Writing runtime SQL for node "model.airbyte_utils.sla_policies_filter"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.195976 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.196112 (Thread-1): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     insert into "datalake".zendesk."sla_policies_filter" ("_airbyte_sla_policies_hashid", "all", "any", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_filter_hashid")
2022-04-19 13:15:48 normalization >     (
2022-04-19 13:15:48 normalization >        select "_airbyte_sla_policies_hashid", "all", "any", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_filter_hashid"
2022-04-19 13:15:48 normalization >        from "sla_policies_filter__dbt_tmp131520601550"
2022-04-19 13:15:48 normalization >     );
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.265106 (Thread-3): SQL status: SELECT in 0.19 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.270284 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.270447 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_policy_metrics'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_policy_metrics'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_policy_metrics'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.316650 (Thread-2): SQL status: INSERT 0 163043 in 1.24 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.321516 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.321680 (Thread-2): On model.airbyte_utils.ticket_metric_events: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metric_events"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_metric_events'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_metric_events'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_metric_events'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.388854 (Thread-1): SQL status: INSERT 0 11 in 0.19 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.393674 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.393843 (Thread-1): On model.airbyte_utils.sla_policies_filter: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_filter'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_filter'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_filter'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.539730 (Thread-3): SQL status: SELECT in 0.27 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.545034 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.545219 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_policy_metrics__dbt_tmp131521102959'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_policy_metrics__dbt_tmp131521102959'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'None'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_policy_metrics__dbt_tmp131521102959'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.601177 (Thread-2): SQL status: SELECT in 0.28 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.604004 (Thread-2): On model.airbyte_utils.ticket_metric_events: COMMIT
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.604169 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metric_events".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.604332 (Thread-2): On model.airbyte_utils.ticket_metric_events: COMMIT
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.677718 (Thread-1): SQL status: SELECT in 0.28 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.680079 (Thread-1): On model.airbyte_utils.sla_policies_filter: COMMIT
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.680249 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.680427 (Thread-1): On model.airbyte_utils.sla_policies_filter: COMMIT
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.744816 (Thread-3): SQL status: SELECT in 0.20 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.751811 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.752043 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_policy_metrics'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_policy_metrics'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_policy_metrics'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.814389 (Thread-2): SQL status: COMMIT in 0.21 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.815287 (Thread-2): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.815516 (Thread-2): On model.airbyte_utils.ticket_metric_events: Close
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.816365 (Thread-2): 13:15:22 | 12 of 33 OK created incremental model zendesk.ticket_metric_events........................................... [INSERT 0 163043 in 5.04s]
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.817201 (Thread-2): Finished running node model.airbyte_utils.ticket_metric_events
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.817491 (Thread-2): Began running node model.airbyte_utils.tickets_custom_fields
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.817859 (Thread-2): 13:15:22 | 16 of 33 SKIP relation zendesk.tickets_custom_fields......................................................... [SKIP]
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.818470 (Thread-2): Finished running node model.airbyte_utils.tickets_custom_fields
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.818683 (Thread-2): Began running node model.airbyte_utils.ticket_fields
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.819054 (Thread-2): 13:15:22 | 17 of 33 START incremental model zendesk.ticket_fields....................................................... [RUN]
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.819840 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.ticket_fields".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.820026 (Thread-2): Compiling model.airbyte_utils.ticket_fields
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.903757 (Thread-1): SQL status: COMMIT in 0.22 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.904738 (Thread-1): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.904981 (Thread-1): On model.airbyte_utils.sla_policies_filter: Close
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.908381 (Thread-1): 13:15:22 | 14 of 33 OK created incremental model zendesk.sla_policies_filter............................................ [INSERT 0 11 in 2.82s]
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.908559 (Thread-3): SQL status: SELECT in 0.16 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.913838 (Thread-3): 
2022-04-19 13:15:48 normalization >     In "datalake"."zendesk"."sla_policies_policy_metrics":
2022-04-19 13:15:48 normalization >         Schema changed: False
2022-04-19 13:15:48 normalization >         Source columns not in target: []
2022-04-19 13:15:48 normalization >         Target columns not in source: []
2022-04-19 13:15:48 normalization >         New column types: []
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.919119 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.927163 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_policy_metrics'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_policy_metrics'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_policy_metrics'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.919322 (Thread-1): Finished running node model.airbyte_utils.sla_policies_filter
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.927513 (Thread-1): Began running node model.airbyte_utils.ticket_fields_custom_field_options_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.927918 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_custom_field_options_ab1".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.928095 (Thread-1): Compiling model.airbyte_utils.ticket_fields_custom_field_options_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.938540 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options_ab1".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.943266 (Thread-1): On model.airbyte_utils.ticket_fields_custom_field_options_ab1: BEGIN
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.943416 (Thread-1): Opening a new connection, currently in state closed
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.943092 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.943733 (Thread-2): On model.airbyte_utils.ticket_fields: BEGIN
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.943872 (Thread-2): Opening a new connection, currently in state closed
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.944023 (Thread-2): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.943537 (Thread-1): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.986374 (Thread-2): SQL status: BEGIN in 0.04 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.986658 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.986795 (Thread-2): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_fields'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_fields'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_fields'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.998311 (Thread-1): SQL status: BEGIN in 0.05 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.998511 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options_ab1".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:22.998636 (Thread-1): On model.airbyte_utils.ticket_fields_custom_field_options_ab1: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options_ab1"} */
2022-04-19 13:15:48 normalization > with max_value as (
2022-04-19 13:15:48 normalization >             select max(json_array_length(custom_field_options, true)) as max_number_of_items
2022-04-19 13:15:48 normalization >             from "datalake".zendesk."ticket_fields_scd"
2022-04-19 13:15:48 normalization >         )
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >             case when max_number_of_items is not null and max_number_of_items > 1
2022-04-19 13:15:48 normalization >             then max_number_of_items
2022-04-19 13:15:48 normalization >             else 1 end as max_number_of_items
2022-04-19 13:15:48 normalization >         from max_value
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.063928 (Thread-1): SQL status: SELECT in 0.07 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.071354 (Thread-1): Writing injected SQL for node "model.airbyte_utils.ticket_fields_custom_field_options_ab1"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.071805 (Thread-1): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.072009 (Thread-1): On model.airbyte_utils.ticket_fields_custom_field_options_ab1: ROLLBACK
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.075856 (Thread-1): On model.airbyte_utils.ticket_fields_custom_field_options_ab1: Close
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.076603 (Thread-1): Finished running node model.airbyte_utils.ticket_fields_custom_field_options_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.076838 (Thread-1): Began running node model.airbyte_utils.tickets_satisfaction_rating
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.077158 (Thread-1): 13:15:23 | 18 of 33 SKIP relation zendesk.tickets_satisfaction_rating................................................... [SKIP]
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.077873 (Thread-1): Finished running node model.airbyte_utils.tickets_satisfaction_rating
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.078171 (Thread-1): Began running node model.airbyte_utils.tickets_via
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.078675 (Thread-1): 13:15:23 | 19 of 33 SKIP relation zendesk.tickets_via................................................................... [SKIP]
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.078969 (Thread-1): Finished running node model.airbyte_utils.tickets_via
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.079286 (Thread-1): Began running node model.airbyte_utils.sla_policies_filter_all_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.079797 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_all_ab1".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.080060 (Thread-1): Compiling model.airbyte_utils.sla_policies_filter_all_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.091005 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter_all_ab1".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.091195 (Thread-1): On model.airbyte_utils.sla_policies_filter_all_ab1: BEGIN
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.091338 (Thread-1): Opening a new connection, currently in state closed
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.091451 (Thread-1): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.145309 (Thread-1): SQL status: BEGIN in 0.05 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.145614 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter_all_ab1".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.145748 (Thread-1): On model.airbyte_utils.sla_policies_filter_all_ab1: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all_ab1"} */
2022-04-19 13:15:48 normalization > with max_value as (
2022-04-19 13:15:48 normalization >             select max(json_array_length("all", true)) as max_number_of_items
2022-04-19 13:15:48 normalization >             from "datalake".zendesk."sla_policies_filter"
2022-04-19 13:15:48 normalization >         )
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >             case when max_number_of_items is not null and max_number_of_items > 1
2022-04-19 13:15:48 normalization >             then max_number_of_items
2022-04-19 13:15:48 normalization >             else 1 end as max_number_of_items
2022-04-19 13:15:48 normalization >         from max_value
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.233480 (Thread-1): SQL status: SELECT in 0.09 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.239350 (Thread-1): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_all_ab1"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.239723 (Thread-1): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.239925 (Thread-1): On model.airbyte_utils.sla_policies_filter_all_ab1: ROLLBACK
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.243417 (Thread-1): On model.airbyte_utils.sla_policies_filter_all_ab1: Close
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.244029 (Thread-1): Finished running node model.airbyte_utils.sla_policies_filter_all_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.244271 (Thread-1): Began running node model.airbyte_utils.sla_policies_filter_any_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.244909 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_any_ab1".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.245201 (Thread-1): Compiling model.airbyte_utils.sla_policies_filter_any_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.252635 (Thread-3): SQL status: SELECT in 0.33 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.254697 (Thread-3): Writing runtime SQL for node "model.airbyte_utils.sla_policies_policy_metrics"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.255040 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.255181 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     insert into "datalake".zendesk."sla_policies_policy_metrics" ("_airbyte_sla_policies_hashid", "metric", "target", "priority", "business_hours", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_policy_metrics_hashid")
2022-04-19 13:15:48 normalization >     (
2022-04-19 13:15:48 normalization >        select "_airbyte_sla_policies_hashid", "metric", "target", "priority", "business_hours", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_policy_metrics_hashid"
2022-04-19 13:15:48 normalization >        from "sla_policies_policy_metrics__dbt_tmp131521102959"
2022-04-19 13:15:48 normalization >     );
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.260142 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter_any_ab1".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.260321 (Thread-1): On model.airbyte_utils.sla_policies_filter_any_ab1: BEGIN
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.260464 (Thread-1): Opening a new connection, currently in state closed
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.260578 (Thread-1): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.325654 (Thread-1): SQL status: BEGIN in 0.07 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.325937 (Thread-1): Using redshift connection "model.airbyte_utils.sla_policies_filter_any_ab1".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.326069 (Thread-1): On model.airbyte_utils.sla_policies_filter_any_ab1: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any_ab1"} */
2022-04-19 13:15:48 normalization > with max_value as (
2022-04-19 13:15:48 normalization >             select max(json_array_length("any", true)) as max_number_of_items
2022-04-19 13:15:48 normalization >             from "datalake".zendesk."sla_policies_filter"
2022-04-19 13:15:48 normalization >         )
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >             case when max_number_of_items is not null and max_number_of_items > 1
2022-04-19 13:15:48 normalization >             then max_number_of_items
2022-04-19 13:15:48 normalization >             else 1 end as max_number_of_items
2022-04-19 13:15:48 normalization >         from max_value
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.356207 (Thread-2): SQL status: SELECT in 0.37 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.359480 (Thread-2): Writing injected SQL for node "model.airbyte_utils.ticket_fields"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.359831 (Thread-2): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.367514 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.367675 (Thread-2): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_fields'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_fields'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_fields'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.507985 (Thread-2): SQL status: SELECT in 0.14 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.514024 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.514213 (Thread-2): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >   create temporary table
2022-04-19 13:15:48 normalization >     "ticket_fields__dbt_tmp131523364395"
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >       compound sortkey(_airbyte_unique_key,_airbyte_emitted_at)
2022-04-19 13:15:48 normalization >   as (
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > -- Final base SQL model
2022-04-19 13:15:48 normalization > -- depends_on: "datalake".zendesk."ticket_fields_scd"
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     _airbyte_unique_key,
2022-04-19 13:15:48 normalization >     id,
2022-04-19 13:15:48 normalization >     "tag",
2022-04-19 13:15:48 normalization >     url,
2022-04-19 13:15:48 normalization >     type,
2022-04-19 13:15:48 normalization >     title,
2022-04-19 13:15:48 normalization >     active,
2022-04-19 13:15:48 normalization >     position,
2022-04-19 13:15:48 normalization >     required,
2022-04-19 13:15:48 normalization >     raw_title,
2022-04-19 13:15:48 normalization >     removable,
2022-04-19 13:15:48 normalization >     created_at,
2022-04-19 13:15:48 normalization >     updated_at,
2022-04-19 13:15:48 normalization >     description,
2022-04-19 13:15:48 normalization >     sub_type_id,
2022-04-19 13:15:48 normalization >     raw_description,
2022-04-19 13:15:48 normalization >     title_in_portal,
2022-04-19 13:15:48 normalization >     agent_description,
2022-04-19 13:15:48 normalization >     visible_in_portal,
2022-04-19 13:15:48 normalization >     editable_in_portal,
2022-04-19 13:15:48 normalization >     required_in_portal,
2022-04-19 13:15:48 normalization >     raw_title_in_portal,
2022-04-19 13:15:48 normalization >     collapsed_for_agents,
2022-04-19 13:15:48 normalization >     custom_field_options,
2022-04-19 13:15:48 normalization >     system_field_options,
2022-04-19 13:15:48 normalization >     regexp_for_validation,
2022-04-19 13:15:48 normalization >     _airbyte_ab_id,
2022-04-19 13:15:48 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:48 normalization >     _airbyte_ticket_fields_hashid
2022-04-19 13:15:48 normalization > from "datalake".zendesk."ticket_fields_scd"
2022-04-19 13:15:48 normalization > -- ticket_fields from "datalake".zendesk._airbyte_raw_ticket_fields
2022-04-19 13:15:48 normalization > where 1 = 1
2022-04-19 13:15:48 normalization > and _airbyte_active_row = 1
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > and coalesce(
2022-04-19 13:15:48 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:48 normalization >     timestamp with time zone
2022-04-19 13:15:48 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:48 normalization >     timestamp with time zone
2022-04-19 13:15:48 normalization > )) from "datalake".zendesk."ticket_fields"),
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     true)
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >   );
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.520313 (Thread-3): SQL status: INSERT 0 264 in 0.26 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.525018 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.525187 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_policy_metrics"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_policy_metrics'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_policy_metrics'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_policy_metrics'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.550582 (Thread-1): SQL status: SELECT in 0.22 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.558520 (Thread-1): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_any_ab1"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.558972 (Thread-1): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.559203 (Thread-1): On model.airbyte_utils.sla_policies_filter_any_ab1: ROLLBACK
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.562709 (Thread-1): On model.airbyte_utils.sla_policies_filter_any_ab1: Close
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.563443 (Thread-1): Finished running node model.airbyte_utils.sla_policies_filter_any_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.563710 (Thread-1): Began running node model.airbyte_utils.ticket_fields_custom_field_options_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.564186 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_custom_field_options_ab2".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.565563 (Thread-1): Compiling model.airbyte_utils.ticket_fields_custom_field_options_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.595074 (Thread-1): Writing injected SQL for node "model.airbyte_utils.ticket_fields_custom_field_options_ab2"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.595588 (Thread-1): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.596158 (Thread-1): Finished running node model.airbyte_utils.ticket_fields_custom_field_options_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.596418 (Thread-1): Began running node model.airbyte_utils.tickets_via_source_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.598484 (Thread-1): Finished running node model.airbyte_utils.tickets_via_source_ab1
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.598692 (Thread-1): Began running node model.airbyte_utils.sla_policies_filter_all_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.600052 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_all_ab2".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.600238 (Thread-1): Compiling model.airbyte_utils.sla_policies_filter_all_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.629570 (Thread-1): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_all_ab2"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.630055 (Thread-1): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.630556 (Thread-1): Finished running node model.airbyte_utils.sla_policies_filter_all_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.630777 (Thread-1): Began running node model.airbyte_utils.sla_policies_filter_any_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.631551 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_any_ab2".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.631772 (Thread-1): Compiling model.airbyte_utils.sla_policies_filter_any_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.646548 (Thread-1): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_any_ab2"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.646900 (Thread-1): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.647444 (Thread-1): Finished running node model.airbyte_utils.sla_policies_filter_any_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.647663 (Thread-1): Began running node model.airbyte_utils.ticket_fields_custom_field_options_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.648040 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_custom_field_options_ab3".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.648408 (Thread-1): Compiling model.airbyte_utils.ticket_fields_custom_field_options_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.672542 (Thread-1): Writing injected SQL for node "model.airbyte_utils.ticket_fields_custom_field_options_ab3"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.673048 (Thread-1): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.673602 (Thread-1): Finished running node model.airbyte_utils.ticket_fields_custom_field_options_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.673846 (Thread-1): Began running node model.airbyte_utils.tickets_via_source_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.674380 (Thread-1): Finished running node model.airbyte_utils.tickets_via_source_ab2
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.674574 (Thread-1): Began running node model.airbyte_utils.sla_policies_filter_all_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.675144 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_all_ab3".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.675420 (Thread-1): Compiling model.airbyte_utils.sla_policies_filter_all_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.693451 (Thread-1): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_all_ab3"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.693938 (Thread-1): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.694541 (Thread-1): Finished running node model.airbyte_utils.sla_policies_filter_all_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.694767 (Thread-1): Began running node model.airbyte_utils.sla_policies_filter_any_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.695154 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_any_ab3".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.695522 (Thread-1): Compiling model.airbyte_utils.sla_policies_filter_any_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.713045 (Thread-1): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_any_ab3"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.713526 (Thread-1): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.714149 (Thread-1): Finished running node model.airbyte_utils.sla_policies_filter_any_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.714381 (Thread-1): Began running node model.airbyte_utils.ticket_fields_custom_field_options
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.714912 (Thread-1): 13:15:23 | 20 of 33 START incremental model zendesk.ticket_fields_custom_field_options.................................. [RUN]
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.715802 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.716042 (Thread-1): Compiling model.airbyte_utils.ticket_fields_custom_field_options
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.731358 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.731631 (Thread-1): On model.airbyte_utils.ticket_fields_custom_field_options: BEGIN
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.731831 (Thread-1): Opening a new connection, currently in state closed
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.731956 (Thread-1): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.797143 (Thread-1): SQL status: BEGIN in 0.07 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.797446 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.797584 (Thread-1): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_fields_custom_field_options'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_fields_custom_field_options'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_fields_custom_field_options'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.802438 (Thread-3): SQL status: SELECT in 0.28 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.804806 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: COMMIT
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.804975 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_policy_metrics".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.805105 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: COMMIT
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.931172 (Thread-3): SQL status: COMMIT in 0.13 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.932052 (Thread-3): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.932303 (Thread-3): On model.airbyte_utils.sla_policies_policy_metrics: Close
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.933132 (Thread-3): 13:15:23 | 15 of 33 OK created incremental model zendesk.sla_policies_policy_metrics.................................... [INSERT 0 264 in 3.31s]
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.934049 (Thread-3): Finished running node model.airbyte_utils.sla_policies_policy_metrics
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.934320 (Thread-3): Began running node model.airbyte_utils.tickets_via_source_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.934656 (Thread-3): Finished running node model.airbyte_utils.tickets_via_source_ab3
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.934843 (Thread-3): Began running node model.airbyte_utils.sla_policies_filter_all
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.935317 (Thread-3): 13:15:23 | 21 of 33 START incremental model zendesk.sla_policies_filter_all............................................. [RUN]
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.936100 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.936310 (Thread-3): Compiling model.airbyte_utils.sla_policies_filter_all
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.953002 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.953234 (Thread-3): On model.airbyte_utils.sla_policies_filter_all: BEGIN
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.953379 (Thread-3): Opening a new connection, currently in state closed
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:23.953493 (Thread-3): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.009387 (Thread-3): SQL status: BEGIN in 0.06 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.009697 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.009830 (Thread-3): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_filter_all'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_filter_all'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_filter_all'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.260999 (Thread-1): SQL status: SELECT in 0.46 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.272758 (Thread-1): Writing injected SQL for node "model.airbyte_utils.ticket_fields_custom_field_options"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.273154 (Thread-1): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.280367 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.280530 (Thread-1): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_fields_custom_field_options'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_fields_custom_field_options'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_fields_custom_field_options'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.378754 (Thread-3): SQL status: SELECT in 0.37 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.389736 (Thread-3): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_all"
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.390439 (Thread-3): finished collecting timing info
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.400964 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.401179 (Thread-3): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_filter_all'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_filter_all'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_filter_all'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.595770 (Thread-1): SQL status: SELECT in 0.32 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.601503 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.601708 (Thread-1): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >   create temporary table
2022-04-19 13:15:48 normalization >     "ticket_fields_custom_field_options__dbt_tmp131524277362"
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >       compound sortkey(_airbyte_emitted_at)
2022-04-19 13:15:48 normalization >   as (
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > with __dbt__cte__ticket_fields_custom_field_options_ab1 as (
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:48 normalization > -- depends_on: "datalake".zendesk."ticket_fields_scd"
2022-04-19 13:15:48 normalization > with numbers as (
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     with p as (
2022-04-19 13:15:48 normalization >         select 0 as generated_number union all select 1
2022-04-19 13:15:48 normalization >     ), unioned as (
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p0.generated_number * power(2, 0)
2022-04-19 13:15:48 normalization >      + 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p1.generated_number * power(2, 1)
2022-04-19 13:15:48 normalization >      + 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p2.generated_number * power(2, 2)
2022-04-19 13:15:48 normalization >      + 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p3.generated_number * power(2, 3)
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     + 1
2022-04-19 13:15:48 normalization >     as generated_number
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p as p0
2022-04-19 13:15:48 normalization >      cross join 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p as p1
2022-04-19 13:15:48 normalization >      cross join 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p as p2
2022-04-19 13:15:48 normalization >      cross join 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p as p3
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select *
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     where generated_number <= 16
2022-04-19 13:15:48 normalization >     order by generated_number
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > joined as (
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >         _airbyte_ticket_fields_hashid as _airbyte_hashid,
2022-04-19 13:15:48 normalization >         json_extract_array_element_text(custom_field_options, numbers.generated_number::int - 1, true) as _airbyte_nested_data
2022-04-19 13:15:48 normalization >     from "datalake".zendesk."ticket_fields_scd"
2022-04-19 13:15:48 normalization >     cross join numbers
2022-04-19 13:15:48 normalization >     -- only generate the number of records in the cross join that corresponds
2022-04-19 13:15:48 normalization >     -- to the number of items in "datalake".zendesk."ticket_fields_scd".custom_field_options
2022-04-19 13:15:48 normalization >     where numbers.generated_number <= json_array_length(custom_field_options, true)
2022-04-19 13:15:48 normalization > )
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     _airbyte_ticket_fields_hashid,
2022-04-19 13:15:48 normalization >     case when json_extract_path_text(_airbyte_nested_data, 'id', true) != '' then json_extract_path_text(_airbyte_nested_data, 'id', true) end as id,
2022-04-19 13:15:48 normalization >     case when json_extract_path_text(_airbyte_nested_data, 'name', true) != '' then json_extract_path_text(_airbyte_nested_data, 'name', true) end as name,
2022-04-19 13:15:48 normalization >     case when json_extract_path_text(_airbyte_nested_data, 'value', true) != '' then json_extract_path_text(_airbyte_nested_data, 'value', true) end as value,
2022-04-19 13:15:48 normalization >     case when json_extract_path_text(_airbyte_nested_data, 'default', true) != '' then json_extract_path_text(_airbyte_nested_data, 'default', true) end as "default",
2022-04-19 13:15:48 normalization >     case when json_extract_path_text(_airbyte_nested_data, 'raw_name', true) != '' then json_extract_path_text(_airbyte_nested_data, 'raw_name', true) end as raw_name,
2022-04-19 13:15:48 normalization >     _airbyte_ab_id,
2022-04-19 13:15:48 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:48 normalization > from "datalake".zendesk."ticket_fields_scd" as table_alias
2022-04-19 13:15:48 normalization > -- custom_field_options at ticket_fields/custom_field_options
2022-04-19 13:15:48 normalization > left join joined on _airbyte_ticket_fields_hashid = joined._airbyte_hashid
2022-04-19 13:15:48 normalization > where 1 = 1
2022-04-19 13:15:48 normalization > and custom_field_options is not null
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > ),  __dbt__cte__ticket_fields_custom_field_options_ab2 as (
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:48 normalization > -- depends_on: __dbt__cte__ticket_fields_custom_field_options_ab1
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     _airbyte_ticket_fields_hashid,
2022-04-19 13:15:48 normalization >     cast(id as 
2022-04-19 13:15:48 normalization >     bigint
2022-04-19 13:15:48 normalization > ) as id,
2022-04-19 13:15:48 normalization >     cast(name as varchar) as name,
2022-04-19 13:15:48 normalization >     cast(value as varchar) as value,
2022-04-19 13:15:48 normalization >     cast(decode("default", 'true', '1', 'false', '0')::integer as boolean) as "default",
2022-04-19 13:15:48 normalization >     cast(raw_name as varchar) as raw_name,
2022-04-19 13:15:48 normalization >     _airbyte_ab_id,
2022-04-19 13:15:48 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:48 normalization > from __dbt__cte__ticket_fields_custom_field_options_ab1
2022-04-19 13:15:48 normalization > -- custom_field_options at ticket_fields/custom_field_options
2022-04-19 13:15:48 normalization > where 1 = 1
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > ),  __dbt__cte__ticket_fields_custom_field_options_ab3 as (
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > -- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:48 normalization > -- depends_on: __dbt__cte__ticket_fields_custom_field_options_ab2
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     md5(cast(coalesce(cast(_airbyte_ticket_fields_hashid as varchar), '') || '-' || coalesce(cast(id as varchar), '') || '-' || coalesce(cast(name as varchar), '') || '-' || coalesce(cast(value as varchar), '') || '-' || coalesce(cast(case when "default" then 'true' else 'false' end as varchar), '') || '-' || coalesce(cast(raw_name as varchar), '') as varchar)) as _airbyte_custom_field_options_hashid,
2022-04-19 13:15:48 normalization >     tmp.*
2022-04-19 13:15:48 normalization > from __dbt__cte__ticket_fields_custom_field_options_ab2 tmp
2022-04-19 13:15:48 normalization > -- custom_field_options at ticket_fields/custom_field_options
2022-04-19 13:15:48 normalization > where 1 = 1
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > )-- Final base SQL model
2022-04-19 13:15:48 normalization > -- depends_on: __dbt__cte__ticket_fields_custom_field_options_ab3
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     _airbyte_ticket_fields_hashid,
2022-04-19 13:15:48 normalization >     id,
2022-04-19 13:15:48 normalization >     name,
2022-04-19 13:15:48 normalization >     value,
2022-04-19 13:15:48 normalization >     "default",
2022-04-19 13:15:48 normalization >     raw_name,
2022-04-19 13:15:48 normalization >     _airbyte_ab_id,
2022-04-19 13:15:48 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:48 normalization >     _airbyte_custom_field_options_hashid
2022-04-19 13:15:48 normalization > from __dbt__cte__ticket_fields_custom_field_options_ab3
2022-04-19 13:15:48 normalization > -- custom_field_options at ticket_fields/custom_field_options from "datalake".zendesk."ticket_fields_scd"
2022-04-19 13:15:48 normalization > where 1 = 1
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > and coalesce(
2022-04-19 13:15:48 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:48 normalization >     timestamp with time zone
2022-04-19 13:15:48 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:48 normalization >     timestamp with time zone
2022-04-19 13:15:48 normalization > )) from "datalake".zendesk."ticket_fields_custom_field_options"),
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     true)
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >   );
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.807130 (Thread-3): SQL status: SELECT in 0.41 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.812620 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:24.812786 (Thread-3): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >   create temporary table
2022-04-19 13:15:48 normalization >     "sla_policies_filter_all__dbt_tmp131524394864"
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >       compound sortkey(_airbyte_emitted_at)
2022-04-19 13:15:48 normalization >   as (
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > with __dbt__cte__sla_policies_filter_all_ab1 as (
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:48 normalization > -- depends_on: "datalake".zendesk."sla_policies_filter"
2022-04-19 13:15:48 normalization > with numbers as (
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     with p as (
2022-04-19 13:15:48 normalization >         select 0 as generated_number union all select 1
2022-04-19 13:15:48 normalization >     ), unioned as (
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p0.generated_number * power(2, 0)
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     + 1
2022-04-19 13:15:48 normalization >     as generated_number
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     p as p0
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select *
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     where generated_number <= 1
2022-04-19 13:15:48 normalization >     order by generated_number
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > ),
2022-04-19 13:15:48 normalization > joined as (
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >         _airbyte_filter_hashid as _airbyte_hashid,
2022-04-19 13:15:48 normalization >         json_extract_array_element_text("all", numbers.generated_number::int - 1, true) as _airbyte_nested_data
2022-04-19 13:15:48 normalization >     from "datalake".zendesk."sla_policies_filter"
2022-04-19 13:15:48 normalization >     cross join numbers
2022-04-19 13:15:48 normalization >     -- only generate the number of records in the cross join that corresponds
2022-04-19 13:15:48 normalization >     -- to the number of items in "datalake".zendesk."sla_policies_filter"."all"
2022-04-19 13:15:48 normalization >     where numbers.generated_number <= json_array_length("all", true)
2022-04-19 13:15:48 normalization > )
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     _airbyte_filter_hashid,
2022-04-19 13:15:48 normalization >     case when json_extract_path_text(_airbyte_nested_data, 'field', true) != '' then json_extract_path_text(_airbyte_nested_data, 'field', true) end as field,
2022-04-19 13:15:48 normalization >     case when json_extract_path_text(_airbyte_nested_data, 'value', true) != '' then json_extract_path_text(_airbyte_nested_data, 'value', true) end as value,
2022-04-19 13:15:48 normalization >     case when json_extract_path_text(_airbyte_nested_data, 'operator', true) != '' then json_extract_path_text(_airbyte_nested_data, 'operator', true) end as operator,
2022-04-19 13:15:48 normalization >     _airbyte_ab_id,
2022-04-19 13:15:48 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:48 normalization > from "datalake".zendesk."sla_policies_filter" as table_alias
2022-04-19 13:15:48 normalization > -- all at sla_policies/filter/all
2022-04-19 13:15:48 normalization > left join joined on _airbyte_filter_hashid = joined._airbyte_hashid
2022-04-19 13:15:48 normalization > where 1 = 1
2022-04-19 13:15:48 normalization > and "all" is not null
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > ),  __dbt__cte__sla_policies_filter_all_ab2 as (
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:48 normalization > -- depends_on: __dbt__cte__sla_policies_filter_all_ab1
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     _airbyte_filter_hashid,
2022-04-19 13:15:48 normalization >     cast(field as varchar) as field,
2022-04-19 13:15:48 normalization >     cast(value as varchar) as value,
2022-04-19 13:15:48 normalization >     cast(operator as varchar) as operator,
2022-04-19 13:15:48 normalization >     _airbyte_ab_id,
2022-04-19 13:15:48 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:48 normalization > from __dbt__cte__sla_policies_filter_all_ab1
2022-04-19 13:15:48 normalization > -- all at sla_policies/filter/all
2022-04-19 13:15:48 normalization > where 1 = 1
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > ),  __dbt__cte__sla_policies_filter_all_ab3 as (
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > -- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:48 normalization > -- depends_on: __dbt__cte__sla_policies_filter_all_ab2
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     md5(cast(coalesce(cast(_airbyte_filter_hashid as varchar), '') || '-' || coalesce(cast(field as varchar), '') || '-' || coalesce(cast(value as varchar), '') || '-' || coalesce(cast(operator as varchar), '') as varchar)) as _airbyte_all_hashid,
2022-04-19 13:15:48 normalization >     tmp.*
2022-04-19 13:15:48 normalization > from __dbt__cte__sla_policies_filter_all_ab2 tmp
2022-04-19 13:15:48 normalization > -- all at sla_policies/filter/all
2022-04-19 13:15:48 normalization > where 1 = 1
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > )-- Final base SQL model
2022-04-19 13:15:48 normalization > -- depends_on: __dbt__cte__sla_policies_filter_all_ab3
2022-04-19 13:15:48 normalization > select
2022-04-19 13:15:48 normalization >     _airbyte_filter_hashid,
2022-04-19 13:15:48 normalization >     field,
2022-04-19 13:15:48 normalization >     value,
2022-04-19 13:15:48 normalization >     operator,
2022-04-19 13:15:48 normalization >     _airbyte_ab_id,
2022-04-19 13:15:48 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:48 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:48 normalization >     _airbyte_all_hashid
2022-04-19 13:15:48 normalization > from __dbt__cte__sla_policies_filter_all_ab3
2022-04-19 13:15:48 normalization > -- all at sla_policies/filter/all from "datalake".zendesk."sla_policies_filter"
2022-04-19 13:15:48 normalization > where 1 = 1
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization > and coalesce(
2022-04-19 13:15:48 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:48 normalization >     timestamp with time zone
2022-04-19 13:15:48 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:48 normalization >     timestamp with time zone
2022-04-19 13:15:48 normalization > )) from "datalake".zendesk."sla_policies_filter_all"),
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     true)
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >   );
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:25.172084 (Thread-3): SQL status: SELECT in 0.36 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:25.176824 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:25.176992 (Thread-3): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_filter_all__dbt_tmp131524394864'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_filter_all__dbt_tmp131524394864'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'None'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_filter_all__dbt_tmp131524394864'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:25.339924 (Thread-1): SQL status: SELECT in 0.74 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:25.344631 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:25.344796 (Thread-1): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_fields_custom_field_options__dbt_tmp131524277362'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_fields_custom_field_options__dbt_tmp131524277362'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'None'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_fields_custom_field_options__dbt_tmp131524277362'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:25.422481 (Thread-3): SQL status: SELECT in 0.25 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:25.427710 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:25.427884 (Thread-3): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_filter_all'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_filter_all'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_filter_all'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:25.705213 (Thread-1): SQL status: SELECT in 0.36 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:25.710173 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:25.710353 (Thread-1): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_fields_custom_field_options'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_fields_custom_field_options'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_fields_custom_field_options'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:25.792944 (Thread-3): SQL status: SELECT in 0.36 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:25.798500 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:25.798687 (Thread-3): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_filter_all__dbt_tmp131524394864'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_filter_all__dbt_tmp131524394864'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'None'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_filter_all__dbt_tmp131524394864'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:26.012876 (Thread-1): SQL status: SELECT in 0.30 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:26.020927 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:26.021144 (Thread-1): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'ticket_fields_custom_field_options__dbt_tmp131524277362'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'ticket_fields_custom_field_options__dbt_tmp131524277362'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'None'
2022-04-19 13:15:48 normalization >         and tablename = 'ticket_fields_custom_field_options__dbt_tmp131524277362'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     select
2022-04-19 13:15:48 normalization >       column_name,
2022-04-19 13:15:48 normalization >       data_type,
2022-04-19 13:15:48 normalization >       character_maximum_length,
2022-04-19 13:15:48 normalization >       numeric_precision,
2022-04-19 13:15:48 normalization >       numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     from unioned
2022-04-19 13:15:48 normalization >     
2022-04-19 13:15:48 normalization >     order by ordinal_position
2022-04-19 13:15:48 normalization >   
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:26.120693 (Thread-3): SQL status: SELECT in 0.32 seconds
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:26.125510 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-19 13:15:48 normalization > 2022-04-19 13:15:26.125686 (Thread-3): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       with bound_views as (
2022-04-19 13:15:48 normalization >         select
2022-04-19 13:15:48 normalization >           ordinal_position,
2022-04-19 13:15:48 normalization >           table_schema,
2022-04-19 13:15:48 normalization >           column_name,
2022-04-19 13:15:48 normalization >           data_type,
2022-04-19 13:15:48 normalization >           character_maximum_length,
2022-04-19 13:15:48 normalization >           numeric_precision,
2022-04-19 13:15:48 normalization >           numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >         from information_schema."columns"
2022-04-19 13:15:48 normalization >         where table_name = 'sla_policies_filter_all'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unbound_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         ordinal_position,
2022-04-19 13:15:48 normalization >         view_schema,
2022-04-19 13:15:48 normalization >         col_name,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:48 normalization >             'character varying'
2022-04-19 13:15:48 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else col_type
2022-04-19 13:15:48 normalization >         end as col_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'character%'
2022-04-19 13:15:48 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:48 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:48 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:48 normalization >       where view_name = 'sla_policies_filter_all'
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     external_views as (
2022-04-19 13:15:48 normalization >       select
2022-04-19 13:15:48 normalization >         columnnum,
2022-04-19 13:15:48 normalization >         schemaname,
2022-04-19 13:15:48 normalization >         columnname,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:48 normalization >           then 'character varying'
2022-04-19 13:15:48 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:48 normalization >           else external_type
2022-04-19 13:15:48 normalization >         end as external_type,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as character_maximum_length,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_precision,
2022-04-19 13:15:48 normalization >         case
2022-04-19 13:15:48 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:48 normalization >           then nullif(
2022-04-19 13:15:48 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:48 normalization >             '')::int
2022-04-19 13:15:48 normalization >           else null
2022-04-19 13:15:48 normalization >         end as numeric_scale
2022-04-19 13:15:48 normalization >       from
2022-04-19 13:15:48 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:48 normalization >       where
2022-04-19 13:15:48 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:48 normalization >         and tablename = 'sla_policies_filter_all'
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     ),
2022-04-19 13:15:48 normalization > 
2022-04-19 13:15:48 normalization >     unioned as (
2022-04-19 13:15:48 normalization >       select * from bound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from unbound_views
2022-04-19 13:15:48 normalization >       union all
2022-04-19 13:15:48 normalization >       select * from external_views
2022-04-19 13:15:48 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:26.503362 (Thread-1): SQL status: SELECT in 0.48 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:26.508584 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:26.508758 (Thread-1): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_fields_custom_field_options'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_fields_custom_field_options'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_fields_custom_field_options'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:26.654907 (Thread-3): SQL status: SELECT in 0.53 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:26.658206 (Thread-3): 
2022-04-19 13:15:49 normalization >     In "datalake"."zendesk"."sla_policies_filter_all":
2022-04-19 13:15:49 normalization >         Schema changed: False
2022-04-19 13:15:49 normalization >         Source columns not in target: []
2022-04-19 13:15:49 normalization >         Target columns not in source: []
2022-04-19 13:15:49 normalization >         New column types: []
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:26.661574 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:26.661750 (Thread-3): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'sla_policies_filter_all'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'sla_policies_filter_all'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'sla_policies_filter_all'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:26.962337 (Thread-1): SQL status: SELECT in 0.45 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:26.965854 (Thread-1): 
2022-04-19 13:15:49 normalization >     In "datalake"."zendesk"."ticket_fields_custom_field_options":
2022-04-19 13:15:49 normalization >         Schema changed: False
2022-04-19 13:15:49 normalization >         Source columns not in target: []
2022-04-19 13:15:49 normalization >         Target columns not in source: []
2022-04-19 13:15:49 normalization >         New column types: []
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:26.966068 (Thread-4): SQL status: SELECT in 10.29 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:26.969363 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:26.973338 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:26.973562 (Thread-1): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_fields_custom_field_options'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_fields_custom_field_options'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_fields_custom_field_options'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:26.973764 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_scd__dbt_tmp131516226018'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_scd__dbt_tmp131516226018'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'None'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_scd__dbt_tmp131516226018'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.045533 (Thread-3): SQL status: SELECT in 0.38 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.047956 (Thread-3): Writing runtime SQL for node "model.airbyte_utils.sla_policies_filter_all"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.048387 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.048528 (Thread-3): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     insert into "datalake".zendesk."sla_policies_filter_all" ("_airbyte_filter_hashid", "field", "value", "operator", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_all_hashid")
2022-04-19 13:15:49 normalization >     (
2022-04-19 13:15:49 normalization >        select "_airbyte_filter_hashid", "field", "value", "operator", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_all_hashid"
2022-04-19 13:15:49 normalization >        from "sla_policies_filter_all__dbt_tmp131524394864"
2022-04-19 13:15:49 normalization >     );
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.251201 (Thread-3): SQL status: INSERT 0 11 in 0.20 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.258556 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.258725 (Thread-3): On model.airbyte_utils.sla_policies_filter_all: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_all"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'sla_policies_filter_all'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'sla_policies_filter_all'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'sla_policies_filter_all'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.409345 (Thread-1): SQL status: SELECT in 0.44 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.411838 (Thread-1): Writing runtime SQL for node "model.airbyte_utils.ticket_fields_custom_field_options"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.412243 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.412433 (Thread-1): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     insert into "datalake".zendesk."ticket_fields_custom_field_options" ("_airbyte_ticket_fields_hashid", "id", "name", "value", "default", "raw_name", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_custom_field_options_hashid")
2022-04-19 13:15:49 normalization >     (
2022-04-19 13:15:49 normalization >        select "_airbyte_ticket_fields_hashid", "id", "name", "value", "default", "raw_name", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_custom_field_options_hashid"
2022-04-19 13:15:49 normalization >        from "ticket_fields_custom_field_options__dbt_tmp131524277362"
2022-04-19 13:15:49 normalization >     );
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.425821 (Thread-4): SQL status: SELECT in 0.45 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.431369 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.431527 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_scd'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_scd'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_scd'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.583794 (Thread-1): SQL status: INSERT 0 202 in 0.17 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.588640 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.588822 (Thread-1): On model.airbyte_utils.ticket_fields_custom_field_options: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields_custom_field_options"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_fields_custom_field_options'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_fields_custom_field_options'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_fields_custom_field_options'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.759176 (Thread-3): SQL status: SELECT in 0.50 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.761781 (Thread-3): On model.airbyte_utils.sla_policies_filter_all: COMMIT
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.761945 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_all".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.762075 (Thread-3): On model.airbyte_utils.sla_policies_filter_all: COMMIT
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.799628 (Thread-4): SQL status: SELECT in 0.37 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.805426 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.805585 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_scd__dbt_tmp131516226018'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_scd__dbt_tmp131516226018'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'None'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_scd__dbt_tmp131516226018'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.840498 (Thread-3): SQL status: COMMIT in 0.08 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.841149 (Thread-3): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.841350 (Thread-3): On model.airbyte_utils.sla_policies_filter_all: Close
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.842108 (Thread-3): 13:15:27 | 21 of 33 OK created incremental model zendesk.sla_policies_filter_all........................................ [INSERT 0 11 in 3.91s]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.842466 (Thread-3): Finished running node model.airbyte_utils.sla_policies_filter_all
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.842677 (Thread-3): Began running node model.airbyte_utils.sla_policies_filter_any
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.843292 (Thread-3): 13:15:27 | 22 of 33 START incremental model zendesk.sla_policies_filter_any............................................. [RUN]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.843874 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.844056 (Thread-3): Compiling model.airbyte_utils.sla_policies_filter_any
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.854444 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.854613 (Thread-3): On model.airbyte_utils.sla_policies_filter_any: BEGIN
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.854751 (Thread-3): Opening a new connection, currently in state closed
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.854863 (Thread-3): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.927616 (Thread-3): SQL status: BEGIN in 0.07 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.927905 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:27.928034 (Thread-3): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'sla_policies_filter_any'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'sla_policies_filter_any'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'sla_policies_filter_any'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.068663 (Thread-1): SQL status: SELECT in 0.48 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.071218 (Thread-1): On model.airbyte_utils.ticket_fields_custom_field_options: COMMIT
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.071421 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_fields_custom_field_options".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.071554 (Thread-1): On model.airbyte_utils.ticket_fields_custom_field_options: COMMIT
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.146284 (Thread-1): SQL status: COMMIT in 0.07 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.147152 (Thread-1): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.147371 (Thread-1): On model.airbyte_utils.ticket_fields_custom_field_options: Close
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.148157 (Thread-1): 13:15:28 | 20 of 33 OK created incremental model zendesk.ticket_fields_custom_field_options............................. [INSERT 0 202 in 4.43s]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.148385 (Thread-1): Finished running node model.airbyte_utils.ticket_fields_custom_field_options
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.148596 (Thread-1): Began running node model.airbyte_utils.tickets_via_source
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.148926 (Thread-1): 13:15:28 | 23 of 33 SKIP relation zendesk.tickets_via_source............................................................ [SKIP]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.149092 (Thread-1): Finished running node model.airbyte_utils.tickets_via_source
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.149614 (Thread-1): Began running node model.airbyte_utils.tickets_via_source_from_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.149803 (Thread-1): Finished running node model.airbyte_utils.tickets_via_source_from_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.149976 (Thread-1): Began running node model.airbyte_utils.tickets_via_source_to_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.150437 (Thread-1): Finished running node model.airbyte_utils.tickets_via_source_to_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.150807 (Thread-1): Began running node model.airbyte_utils.tickets_via_source_from_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.151127 (Thread-1): Finished running node model.airbyte_utils.tickets_via_source_from_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.151457 (Thread-1): Began running node model.airbyte_utils.tickets_via_source_to_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.151792 (Thread-1): Finished running node model.airbyte_utils.tickets_via_source_to_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.151972 (Thread-1): Began running node model.airbyte_utils.tickets_via_source_from_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.152318 (Thread-1): Finished running node model.airbyte_utils.tickets_via_source_from_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.152499 (Thread-1): Began running node model.airbyte_utils.tickets_via_source_to_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.152806 (Thread-1): Finished running node model.airbyte_utils.tickets_via_source_to_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.152980 (Thread-1): Began running node model.airbyte_utils.tickets_via_source_from
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.153283 (Thread-1): 13:15:28 | 24 of 33 SKIP relation zendesk.tickets_via_source_from....................................................... [SKIP]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.153565 (Thread-1): Finished running node model.airbyte_utils.tickets_via_source_from
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.153916 (Thread-1): Began running node model.airbyte_utils.tickets_via_source_to
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.154195 (Thread-1): 13:15:28 | 25 of 33 SKIP relation zendesk.tickets_via_source_to......................................................... [SKIP]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.154476 (Thread-1): Finished running node model.airbyte_utils.tickets_via_source_to
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.222150 (Thread-4): SQL status: SELECT in 0.42 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.231848 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.232044 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_scd'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_scd'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_scd'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.497732 (Thread-4): SQL status: SELECT in 0.26 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.510160 (Thread-4): 
2022-04-19 13:15:49 normalization >     In "datalake"."zendesk"."ticket_metrics_scd":
2022-04-19 13:15:49 normalization >         Schema changed: False
2022-04-19 13:15:49 normalization >         Source columns not in target: []
2022-04-19 13:15:49 normalization >         Target columns not in source: []
2022-04-19 13:15:49 normalization >         New column types: []
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.510371 (Thread-3): SQL status: SELECT in 0.58 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.513357 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.523669 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_scd'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_scd'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_scd'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.523489 (Thread-3): Writing injected SQL for node "model.airbyte_utils.sla_policies_filter_any"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.524146 (Thread-3): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.531076 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.531241 (Thread-3): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'sla_policies_filter_any'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'sla_policies_filter_any'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'sla_policies_filter_any'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.835811 (Thread-3): SQL status: SELECT in 0.30 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.841400 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.841628 (Thread-3): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >   create temporary table
2022-04-19 13:15:49 normalization >     "sla_policies_filter_any__dbt_tmp131528528052"
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >       compound sortkey(_airbyte_emitted_at)
2022-04-19 13:15:49 normalization >   as (
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization > with __dbt__cte__sla_policies_filter_any_ab1 as (
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:49 normalization > -- depends_on: "datalake".zendesk."sla_policies_filter"
2022-04-19 13:15:49 normalization > with numbers as (
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     with p as (
2022-04-19 13:15:49 normalization >         select 0 as generated_number union all select 1
2022-04-19 13:15:49 normalization >     ), unioned as (
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     p0.generated_number * power(2, 0)
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     + 1
2022-04-19 13:15:49 normalization >     as generated_number
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     p as p0
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select *
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     where generated_number <= 1
2022-04-19 13:15:49 normalization >     order by generated_number
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > ),
2022-04-19 13:15:49 normalization > joined as (
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >         _airbyte_filter_hashid as _airbyte_hashid,
2022-04-19 13:15:49 normalization >         json_extract_array_element_text("any", numbers.generated_number::int - 1, true) as _airbyte_nested_data
2022-04-19 13:15:49 normalization >     from "datalake".zendesk."sla_policies_filter"
2022-04-19 13:15:49 normalization >     cross join numbers
2022-04-19 13:15:49 normalization >     -- only generate the number of records in the cross join that corresponds
2022-04-19 13:15:49 normalization >     -- to the number of items in "datalake".zendesk."sla_policies_filter"."any"
2022-04-19 13:15:49 normalization >     where numbers.generated_number <= json_array_length("any", true)
2022-04-19 13:15:49 normalization > )
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     _airbyte_filter_hashid,
2022-04-19 13:15:49 normalization >     case when json_extract_path_text(_airbyte_nested_data, 'field', true) != '' then json_extract_path_text(_airbyte_nested_data, 'field', true) end as field,
2022-04-19 13:15:49 normalization >     case when json_extract_path_text(_airbyte_nested_data, 'value', true) != '' then json_extract_path_text(_airbyte_nested_data, 'value', true) end as value,
2022-04-19 13:15:49 normalization >     case when json_extract_path_text(_airbyte_nested_data, 'operator', true) != '' then json_extract_path_text(_airbyte_nested_data, 'operator', true) end as operator,
2022-04-19 13:15:49 normalization >     _airbyte_ab_id,
2022-04-19 13:15:49 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:49 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:49 normalization > from "datalake".zendesk."sla_policies_filter" as table_alias
2022-04-19 13:15:49 normalization > -- any at sla_policies/filter/any
2022-04-19 13:15:49 normalization > left join joined on _airbyte_filter_hashid = joined._airbyte_hashid
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > and "any" is not null
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > ),  __dbt__cte__sla_policies_filter_any_ab2 as (
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:49 normalization > -- depends_on: __dbt__cte__sla_policies_filter_any_ab1
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     _airbyte_filter_hashid,
2022-04-19 13:15:49 normalization >     cast(field as varchar) as field,
2022-04-19 13:15:49 normalization >     cast(value as varchar) as value,
2022-04-19 13:15:49 normalization >     cast(operator as varchar) as operator,
2022-04-19 13:15:49 normalization >     _airbyte_ab_id,
2022-04-19 13:15:49 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:49 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:49 normalization > from __dbt__cte__sla_policies_filter_any_ab1
2022-04-19 13:15:49 normalization > -- any at sla_policies/filter/any
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > ),  __dbt__cte__sla_policies_filter_any_ab3 as (
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > -- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:49 normalization > -- depends_on: __dbt__cte__sla_policies_filter_any_ab2
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     md5(cast(coalesce(cast(_airbyte_filter_hashid as varchar), '') || '-' || coalesce(cast(field as varchar), '') || '-' || coalesce(cast(value as varchar), '') || '-' || coalesce(cast(operator as varchar), '') as varchar)) as _airbyte_any_hashid,
2022-04-19 13:15:49 normalization >     tmp.*
2022-04-19 13:15:49 normalization > from __dbt__cte__sla_policies_filter_any_ab2 tmp
2022-04-19 13:15:49 normalization > -- any at sla_policies/filter/any
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > )-- Final base SQL model
2022-04-19 13:15:49 normalization > -- depends_on: __dbt__cte__sla_policies_filter_any_ab3
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     _airbyte_filter_hashid,
2022-04-19 13:15:49 normalization >     field,
2022-04-19 13:15:49 normalization >     value,
2022-04-19 13:15:49 normalization >     operator,
2022-04-19 13:15:49 normalization >     _airbyte_ab_id,
2022-04-19 13:15:49 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:49 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:49 normalization >     _airbyte_any_hashid
2022-04-19 13:15:49 normalization > from __dbt__cte__sla_policies_filter_any_ab3
2022-04-19 13:15:49 normalization > -- any at sla_policies/filter/any from "datalake".zendesk."sla_policies_filter"
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > and coalesce(
2022-04-19 13:15:49 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:49 normalization >     timestamp with time zone
2022-04-19 13:15:49 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:49 normalization >     timestamp with time zone
2022-04-19 13:15:49 normalization > )) from "datalake".zendesk."sla_policies_filter_any"),
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     true)
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >   );
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.912517 (Thread-4): SQL status: SELECT in 0.39 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.916199 (Thread-4): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics_scd"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.916689 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:28.916835 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       delete
2022-04-19 13:15:49 normalization >     from "datalake".zendesk."ticket_metrics_scd"
2022-04-19 13:15:49 normalization >     where (_airbyte_unique_key_scd) in (
2022-04-19 13:15:49 normalization >         select (_airbyte_unique_key_scd)
2022-04-19 13:15:49 normalization >         from "ticket_metrics_scd__dbt_tmp131516226018"
2022-04-19 13:15:49 normalization >     );
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     insert into "datalake".zendesk."ticket_metrics_scd" ("_airbyte_unique_key", "_airbyte_unique_key_scd", "id", "url", "time", "type", "metric", "status", "reopens", "replies", "solved_at", "ticket_id", "created_at", "updated_at", "assigned_at", "instance_id", "group_stations", "assignee_stations", "status_updated_at", "assignee_updated_at", "requester_updated_at", "initially_assigned_at", "reply_time_in_minutes", "latest_comment_added_at", "on_hold_time_in_minutes", "agent_wait_time_in_minutes", "requester_wait_time_in_minutes", "full_resolution_time_in_minutes", "first_resolution_time_in_minutes", "_airbyte_start_at", "_airbyte_end_at", "_airbyte_active_row", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_metrics_hashid")
2022-04-19 13:15:49 normalization >     (
2022-04-19 13:15:49 normalization >        select "_airbyte_unique_key", "_airbyte_unique_key_scd", "id", "url", "time", "type", "metric", "status", "reopens", "replies", "solved_at", "ticket_id", "created_at", "updated_at", "assigned_at", "instance_id", "group_stations", "assignee_stations", "status_updated_at", "assignee_updated_at", "requester_updated_at", "initially_assigned_at", "reply_time_in_minutes", "latest_comment_added_at", "on_hold_time_in_minutes", "agent_wait_time_in_minutes", "requester_wait_time_in_minutes", "full_resolution_time_in_minutes", "first_resolution_time_in_minutes", "_airbyte_start_at", "_airbyte_end_at", "_airbyte_active_row", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_metrics_hashid"
2022-04-19 13:15:49 normalization >        from "ticket_metrics_scd__dbt_tmp131516226018"
2022-04-19 13:15:49 normalization >     );
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.160682 (Thread-3): SQL status: SELECT in 0.32 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.165341 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.165552 (Thread-3): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'sla_policies_filter_any__dbt_tmp131528528052'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'sla_policies_filter_any__dbt_tmp131528528052'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'None'
2022-04-19 13:15:49 normalization >         and tablename = 'sla_policies_filter_any__dbt_tmp131528528052'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.420537 (Thread-3): SQL status: SELECT in 0.25 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.425555 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.425732 (Thread-3): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'sla_policies_filter_any'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'sla_policies_filter_any'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'sla_policies_filter_any'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.429781 (Thread-4): SQL status: INSERT 0 29506 in 0.51 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.434070 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.434234 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_scd'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_scd'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_scd'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.773149 (Thread-3): SQL status: SELECT in 0.35 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.780509 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.780722 (Thread-3): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'sla_policies_filter_any__dbt_tmp131528528052'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'sla_policies_filter_any__dbt_tmp131528528052'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'None'
2022-04-19 13:15:49 normalization >         and tablename = 'sla_policies_filter_any__dbt_tmp131528528052'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.873766 (Thread-4): SQL status: SELECT in 0.44 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.877113 (Thread-4): 'soft_unicode' has been renamed to 'soft_str'. The old name will be removed in MarkupSafe 2.1.
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.877486 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.877623 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_scd"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         drop view _airbyte_zendesk.ticket_metrics_stg
2022-04-19 13:15:49 normalization >       
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.883938 (Thread-4): SQL status: DROP VIEW in 0.01 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.885036 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: COMMIT
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.885200 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_scd".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.885332 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: COMMIT
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.977723 (Thread-4): SQL status: COMMIT in 0.09 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.978654 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.978994 (Thread-4): On model.airbyte_utils.ticket_metrics_scd: Close
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.979864 (Thread-4): 13:15:29 | 10 of 33 OK created incremental model zendesk.ticket_metrics_scd............................................. [INSERT 0 29506 in 17.19s]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.980336 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_scd
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.981141 (Thread-1): Began running node model.airbyte_utils.ticket_metrics
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.981439 (Thread-1): 13:15:29 | 26 of 33 START incremental model zendesk.ticket_metrics...................................................... [RUN]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.982075 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.982253 (Thread-1): Compiling model.airbyte_utils.ticket_metrics
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.993569 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.994085 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab1".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:29.994260 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.002354 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab1"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.002741 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.003177 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.003402 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.004877 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.005415 (Thread-1): On model.airbyte_utils.ticket_metrics: BEGIN
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.005553 (Thread-1): Opening a new connection, currently in state closed
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.005665 (Thread-1): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.005257 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab1".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.006142 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.017908 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab1"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.018363 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.018842 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.019060 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.019653 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab1".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.019822 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.028765 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab1"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.029190 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.029646 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.029860 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.030436 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab1".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.030599 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.041112 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab1"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.041550 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.042001 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.042221 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.042942 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab1".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.043111 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.051616 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab1"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.052161 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.052652 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.052867 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.053449 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab1".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.053613 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.063078 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab1"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.063268 (Thread-1): SQL status: BEGIN in 0.06 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.063520 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.063644 (Thread-1): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.064007 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.064467 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.064687 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_status_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.065246 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_status_ab1".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.065410 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_status_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.073606 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_status_ab1"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.073968 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.074384 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_status_ab1
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.074591 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.075141 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab2".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.075306 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.087344 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab2"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.087734 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.088184 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.088422 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.088996 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab2".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.089165 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.103094 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab2"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.103469 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.103908 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.104117 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.104730 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab2".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.104898 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.117479 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab2"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.117898 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.118375 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.118594 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.119168 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab2".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.119334 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.131554 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab2"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.131927 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.132410 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.132628 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.133181 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab2".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.133347 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.145549 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab2"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.145743 (Thread-3): SQL status: SELECT in 0.36 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.150418 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.150582 (Thread-3): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'sla_policies_filter_any'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'sla_policies_filter_any'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'sla_policies_filter_any'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.150986 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.151434 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.151651 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.152215 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab2".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.152408 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.165112 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab2"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.165515 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.165965 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.166177 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_status_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.166739 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_status_ab2".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.166911 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_status_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.180820 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_status_ab2"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.181264 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.181749 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_status_ab2
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.181964 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.182533 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab3".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.182702 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.271852 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab3"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.272415 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.272942 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.273169 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.273786 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab3".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.273964 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.296960 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab3"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.297432 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.297966 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.298201 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.298825 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab3".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.299002 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.314142 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab3"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.314550 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.315041 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.315270 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.315868 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab3".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.316043 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.331881 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab3"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.332315 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.332786 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.333012 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.333596 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab3".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.333770 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.348410 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab3"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.348797 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.349256 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_reply_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.349479 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.350068 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab3".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.350249 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.370462 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab3"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.370935 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.371470 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.371698 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_status_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.372348 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_status_ab3".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.372539 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_status_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.387535 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_status_ab3"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.388386 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.388904 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_status_ab3
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.389129 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.389794 (Thread-4): 13:15:30 | 27 of 33 START incremental model zendesk.ticket_metrics_agent_wait_time_in_minutes........................... [RUN]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.390549 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.390770 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.401495 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.401746 (Thread-4): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: BEGIN
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.401949 (Thread-4): Opening a new connection, currently in state closed
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.402075 (Thread-4): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.480928 (Thread-4): SQL status: BEGIN in 0.08 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.481241 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.481372 (Thread-4): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.511203 (Thread-1): SQL status: SELECT in 0.45 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.514781 (Thread-1): Writing injected SQL for node "model.airbyte_utils.ticket_metrics"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.515150 (Thread-1): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.521759 (Thread-3): SQL status: SELECT in 0.37 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.524484 (Thread-3): 
2022-04-19 13:15:49 normalization >     In "datalake"."zendesk"."sla_policies_filter_any":
2022-04-19 13:15:49 normalization >         Schema changed: False
2022-04-19 13:15:49 normalization >         Source columns not in target: []
2022-04-19 13:15:49 normalization >         Target columns not in source: []
2022-04-19 13:15:49 normalization >         New column types: []
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.529046 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.530555 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.530778 (Thread-3): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'sla_policies_filter_any'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'sla_policies_filter_any'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'sla_policies_filter_any'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.531051 (Thread-1): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.993789 (Thread-3): SQL status: SELECT in 0.46 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.996214 (Thread-3): Writing runtime SQL for node "model.airbyte_utils.sla_policies_filter_any"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.996655 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:30.996793 (Thread-3): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     insert into "datalake".zendesk."sla_policies_filter_any" ("_airbyte_filter_hashid", "field", "value", "operator", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_any_hashid")
2022-04-19 13:15:49 normalization >     (
2022-04-19 13:15:49 normalization >        select "_airbyte_filter_hashid", "field", "value", "operator", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_any_hashid"
2022-04-19 13:15:49 normalization >        from "sla_policies_filter_any__dbt_tmp131528528052"
2022-04-19 13:15:49 normalization >     );
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.011163 (Thread-4): SQL status: SELECT in 0.53 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.021467 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.021912 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.029194 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.029367 (Thread-4): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.033058 (Thread-1): SQL status: SELECT in 0.50 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.038980 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.039153 (Thread-1): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >   create temporary table
2022-04-19 13:15:49 normalization >     "ticket_metrics__dbt_tmp131530519919"
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >       compound sortkey(_airbyte_unique_key,_airbyte_emitted_at)
2022-04-19 13:15:49 normalization >   as (
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization > -- Final base SQL model
2022-04-19 13:15:49 normalization > -- depends_on: "datalake".zendesk."ticket_metrics_scd"
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     _airbyte_unique_key,
2022-04-19 13:15:49 normalization >     id,
2022-04-19 13:15:49 normalization >     url,
2022-04-19 13:15:49 normalization >     "time",
2022-04-19 13:15:49 normalization >     type,
2022-04-19 13:15:49 normalization >     metric,
2022-04-19 13:15:49 normalization >     status,
2022-04-19 13:15:49 normalization >     reopens,
2022-04-19 13:15:49 normalization >     replies,
2022-04-19 13:15:49 normalization >     solved_at,
2022-04-19 13:15:49 normalization >     ticket_id,
2022-04-19 13:15:49 normalization >     created_at,
2022-04-19 13:15:49 normalization >     updated_at,
2022-04-19 13:15:49 normalization >     assigned_at,
2022-04-19 13:15:49 normalization >     instance_id,
2022-04-19 13:15:49 normalization >     group_stations,
2022-04-19 13:15:49 normalization >     assignee_stations,
2022-04-19 13:15:49 normalization >     status_updated_at,
2022-04-19 13:15:49 normalization >     assignee_updated_at,
2022-04-19 13:15:49 normalization >     requester_updated_at,
2022-04-19 13:15:49 normalization >     initially_assigned_at,
2022-04-19 13:15:49 normalization >     reply_time_in_minutes,
2022-04-19 13:15:49 normalization >     latest_comment_added_at,
2022-04-19 13:15:49 normalization >     on_hold_time_in_minutes,
2022-04-19 13:15:49 normalization >     agent_wait_time_in_minutes,
2022-04-19 13:15:49 normalization >     requester_wait_time_in_minutes,
2022-04-19 13:15:49 normalization >     full_resolution_time_in_minutes,
2022-04-19 13:15:49 normalization >     first_resolution_time_in_minutes,
2022-04-19 13:15:49 normalization >     _airbyte_ab_id,
2022-04-19 13:15:49 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:49 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:49 normalization >     _airbyte_ticket_metrics_hashid
2022-04-19 13:15:49 normalization > from "datalake".zendesk."ticket_metrics_scd"
2022-04-19 13:15:49 normalization > -- ticket_metrics from "datalake".zendesk._airbyte_raw_ticket_metrics
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > and _airbyte_active_row = 1
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > and coalesce(
2022-04-19 13:15:49 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:49 normalization >     timestamp with time zone
2022-04-19 13:15:49 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:49 normalization >     timestamp with time zone
2022-04-19 13:15:49 normalization > )) from "datalake".zendesk."ticket_metrics"),
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     true)
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >   );
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.107473 (Thread-3): SQL status: INSERT 0 11 in 0.11 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.112581 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.112758 (Thread-3): On model.airbyte_utils.sla_policies_filter_any: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.sla_policies_filter_any"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'sla_policies_filter_any'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'sla_policies_filter_any'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'sla_policies_filter_any'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.443824 (Thread-4): SQL status: SELECT in 0.41 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.449148 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.449316 (Thread-4): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >   create temporary table
2022-04-19 13:15:49 normalization >     "ticket_metrics_agent_wait_time_in_minutes__dbt_tmp131531026140"
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >       compound sortkey(_airbyte_emitted_at)
2022-04-19 13:15:49 normalization >   as (
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization > with __dbt__cte__ticket_metrics_agent_wait_time_in_minutes_ab1 as (
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:49 normalization > -- depends_on: "datalake".zendesk."ticket_metrics_scd"
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:49 normalization >     case when json_extract_path_text(agent_wait_time_in_minutes, 'business', true) != '' then json_extract_path_text(agent_wait_time_in_minutes, 'business', true) end as business,
2022-04-19 13:15:49 normalization >     case when json_extract_path_text(agent_wait_time_in_minutes, 'calendar', true) != '' then json_extract_path_text(agent_wait_time_in_minutes, 'calendar', true) end as calendar,
2022-04-19 13:15:49 normalization >     _airbyte_ab_id,
2022-04-19 13:15:49 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:49 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:49 normalization > from "datalake".zendesk."ticket_metrics_scd" as table_alias
2022-04-19 13:15:49 normalization > -- agent_wait_time_in_minutes at ticket_metrics/agent_wait_time_in_minutes
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > and agent_wait_time_in_minutes is not null
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > ),  __dbt__cte__ticket_metrics_agent_wait_time_in_minutes_ab2 as (
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:49 normalization > -- depends_on: __dbt__cte__ticket_metrics_agent_wait_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:49 normalization >     cast(business as 
2022-04-19 13:15:49 normalization >     bigint
2022-04-19 13:15:49 normalization > ) as business,
2022-04-19 13:15:49 normalization >     cast(calendar as 
2022-04-19 13:15:49 normalization >     bigint
2022-04-19 13:15:49 normalization > ) as calendar,
2022-04-19 13:15:49 normalization >     _airbyte_ab_id,
2022-04-19 13:15:49 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:49 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:49 normalization > from __dbt__cte__ticket_metrics_agent_wait_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > -- agent_wait_time_in_minutes at ticket_metrics/agent_wait_time_in_minutes
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > ),  __dbt__cte__ticket_metrics_agent_wait_time_in_minutes_ab3 as (
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > -- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:49 normalization > -- depends_on: __dbt__cte__ticket_metrics_agent_wait_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     md5(cast(coalesce(cast(_airbyte_ticket_metrics_hashid as varchar), '') || '-' || coalesce(cast(business as varchar), '') || '-' || coalesce(cast(calendar as varchar), '') as varchar)) as _airbyte_agent_wait_time_in_minutes_hashid,
2022-04-19 13:15:49 normalization >     tmp.*
2022-04-19 13:15:49 normalization > from __dbt__cte__ticket_metrics_agent_wait_time_in_minutes_ab2 tmp
2022-04-19 13:15:49 normalization > -- agent_wait_time_in_minutes at ticket_metrics/agent_wait_time_in_minutes
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > )-- Final base SQL model
2022-04-19 13:15:49 normalization > -- depends_on: __dbt__cte__ticket_metrics_agent_wait_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:49 normalization >     business,
2022-04-19 13:15:49 normalization >     calendar,
2022-04-19 13:15:49 normalization >     _airbyte_ab_id,
2022-04-19 13:15:49 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:49 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:49 normalization >     _airbyte_agent_wait_time_in_minutes_hashid
2022-04-19 13:15:49 normalization > from __dbt__cte__ticket_metrics_agent_wait_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > -- agent_wait_time_in_minutes at ticket_metrics/agent_wait_time_in_minutes from "datalake".zendesk."ticket_metrics_scd"
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > and coalesce(
2022-04-19 13:15:49 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:49 normalization >     timestamp with time zone
2022-04-19 13:15:49 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:49 normalization >     timestamp with time zone
2022-04-19 13:15:49 normalization > )) from "datalake".zendesk."ticket_metrics_agent_wait_time_in_minutes"),
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     true)
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >   );
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.575258 (Thread-3): SQL status: SELECT in 0.46 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.577712 (Thread-3): On model.airbyte_utils.sla_policies_filter_any: COMMIT
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.577884 (Thread-3): Using redshift connection "model.airbyte_utils.sla_policies_filter_any".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.578015 (Thread-3): On model.airbyte_utils.sla_policies_filter_any: COMMIT
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.693224 (Thread-3): SQL status: COMMIT in 0.12 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.694151 (Thread-3): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.694377 (Thread-3): On model.airbyte_utils.sla_policies_filter_any: Close
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.695212 (Thread-3): 13:15:31 | 22 of 33 OK created incremental model zendesk.sla_policies_filter_any........................................ [INSERT 0 11 in 3.85s]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.695567 (Thread-3): Finished running node model.airbyte_utils.sla_policies_filter_any
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.695897 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.696349 (Thread-3): 13:15:31 | 28 of 33 START incremental model zendesk.ticket_metrics_first_resolution_time_in_minutes..................... [RUN]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.696806 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.697001 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.709882 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.710062 (Thread-3): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: BEGIN
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.710210 (Thread-3): Opening a new connection, currently in state closed
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.710331 (Thread-3): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.767251 (Thread-3): SQL status: BEGIN in 0.06 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.767594 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.767743 (Thread-3): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.913804 (Thread-1): SQL status: SELECT in 0.87 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.918220 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:31.918382 (Thread-1): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics__dbt_tmp131530519919'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics__dbt_tmp131530519919'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'None'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics__dbt_tmp131530519919'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.084473 (Thread-3): SQL status: SELECT in 0.32 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.094846 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.095262 (Thread-3): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.102676 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.102840 (Thread-3): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.379739 (Thread-4): SQL status: SELECT in 0.93 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.384316 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.384525 (Thread-4): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_agent_wait_time_in_minutes__dbt_tmp131531026140'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_agent_wait_time_in_minutes__dbt_tmp131531026140'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'None'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_agent_wait_time_in_minutes__dbt_tmp131531026140'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.432386 (Thread-1): SQL status: SELECT in 0.51 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.438260 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.438450 (Thread-1): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.611516 (Thread-3): SQL status: SELECT in 0.51 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.616920 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.617126 (Thread-3): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >   create temporary table
2022-04-19 13:15:49 normalization >     "ticket_metrics_first_resolution_time_in_mi__dbt_tmp131532099552"
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >       compound sortkey(_airbyte_emitted_at)
2022-04-19 13:15:49 normalization >   as (
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization > with __dbt__cte__ticket_metrics_first_resolution_time_in_minutes_ab1 as (
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:49 normalization > -- depends_on: "datalake".zendesk."ticket_metrics_scd"
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:49 normalization >     case when json_extract_path_text(first_resolution_time_in_minutes, 'business', true) != '' then json_extract_path_text(first_resolution_time_in_minutes, 'business', true) end as business,
2022-04-19 13:15:49 normalization >     case when json_extract_path_text(first_resolution_time_in_minutes, 'calendar', true) != '' then json_extract_path_text(first_resolution_time_in_minutes, 'calendar', true) end as calendar,
2022-04-19 13:15:49 normalization >     _airbyte_ab_id,
2022-04-19 13:15:49 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:49 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:49 normalization > from "datalake".zendesk."ticket_metrics_scd" as table_alias
2022-04-19 13:15:49 normalization > -- first_resolution_time_in_minutes at ticket_metrics/first_resolution_time_in_minutes
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > and first_resolution_time_in_minutes is not null
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > ),  __dbt__cte__ticket_metrics_first_resolution_time_in_minutes_ab2 as (
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:49 normalization > -- depends_on: __dbt__cte__ticket_metrics_first_resolution_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:49 normalization >     cast(business as 
2022-04-19 13:15:49 normalization >     bigint
2022-04-19 13:15:49 normalization > ) as business,
2022-04-19 13:15:49 normalization >     cast(calendar as 
2022-04-19 13:15:49 normalization >     bigint
2022-04-19 13:15:49 normalization > ) as calendar,
2022-04-19 13:15:49 normalization >     _airbyte_ab_id,
2022-04-19 13:15:49 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:49 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:49 normalization > from __dbt__cte__ticket_metrics_first_resolution_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > -- first_resolution_time_in_minutes at ticket_metrics/first_resolution_time_in_minutes
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > ),  __dbt__cte__ticket_metrics_first_resolution_time_in_minutes_ab3 as (
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > -- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:49 normalization > -- depends_on: __dbt__cte__ticket_metrics_first_resolution_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     md5(cast(coalesce(cast(_airbyte_ticket_metrics_hashid as varchar), '') || '-' || coalesce(cast(business as varchar), '') || '-' || coalesce(cast(calendar as varchar), '') as varchar)) as _airbyte_first_resolution_time_in_minutes_hashid,
2022-04-19 13:15:49 normalization >     tmp.*
2022-04-19 13:15:49 normalization > from __dbt__cte__ticket_metrics_first_resolution_time_in_minutes_ab2 tmp
2022-04-19 13:15:49 normalization > -- first_resolution_time_in_minutes at ticket_metrics/first_resolution_time_in_minutes
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > )-- Final base SQL model
2022-04-19 13:15:49 normalization > -- depends_on: __dbt__cte__ticket_metrics_first_resolution_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:49 normalization >     business,
2022-04-19 13:15:49 normalization >     calendar,
2022-04-19 13:15:49 normalization >     _airbyte_ab_id,
2022-04-19 13:15:49 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:49 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:49 normalization >     _airbyte_first_resolution_time_in_minutes_hashid
2022-04-19 13:15:49 normalization > from __dbt__cte__ticket_metrics_first_resolution_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > -- first_resolution_time_in_minutes at ticket_metrics/first_resolution_time_in_minutes from "datalake".zendesk."ticket_metrics_scd"
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > and coalesce(
2022-04-19 13:15:49 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:49 normalization >     timestamp with time zone
2022-04-19 13:15:49 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:49 normalization >     timestamp with time zone
2022-04-19 13:15:49 normalization > )) from "datalake".zendesk."ticket_metrics_first_resolution_time_in_minutes"),
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     true)
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >   );
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.800885 (Thread-1): SQL status: SELECT in 0.36 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.806851 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.807075 (Thread-1): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics__dbt_tmp131530519919'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics__dbt_tmp131530519919'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'None'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics__dbt_tmp131530519919'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.854361 (Thread-4): SQL status: SELECT in 0.47 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.861609 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:32.861780 (Thread-4): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.156729 (Thread-4): SQL status: SELECT in 0.29 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.162089 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.162259 (Thread-4): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_agent_wait_time_in_minutes__dbt_tmp131531026140'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_agent_wait_time_in_minutes__dbt_tmp131531026140'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'None'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_agent_wait_time_in_minutes__dbt_tmp131531026140'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.218280 (Thread-1): SQL status: SELECT in 0.41 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.224215 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.224420 (Thread-1): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.375459 (Thread-3): SQL status: SELECT in 0.76 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.380047 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.380220 (Thread-3): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_first_resolution_time_in_mi__dbt_tmp131532099552'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_first_resolution_time_in_mi__dbt_tmp131532099552'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'None'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_first_resolution_time_in_mi__dbt_tmp131532099552'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.737367 (Thread-4): SQL status: SELECT in 0.57 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.742384 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.742547 (Thread-4): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.791876 (Thread-1): SQL status: SELECT in 0.57 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.800083 (Thread-1): 
2022-04-19 13:15:49 normalization >     In "datalake"."zendesk"."ticket_metrics":
2022-04-19 13:15:49 normalization >         Schema changed: True
2022-04-19 13:15:49 normalization >         Source columns not in target: [<Column _airbyte_unique_key (character varying(32))>]
2022-04-19 13:15:49 normalization >         Target columns not in source: []
2022-04-19 13:15:49 normalization >         New column types: []
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.800905 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.801053 (Thread-1): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     alter table "datalake"."zendesk"."ticket_metrics" add column _airbyte_unique_key character varying(32)
2022-04-19 13:15:49 normalization >       
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.815777 (Thread-1): SQL status: ALTER TABLE in 0.01 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.817171 (Thread-1): 
2022-04-19 13:15:49 normalization >     In "datalake"."zendesk"."ticket_metrics":
2022-04-19 13:15:49 normalization >         Schema change approach: sync_all_columns
2022-04-19 13:15:49 normalization >         Columns added: [<Column _airbyte_unique_key (character varying(32))>]
2022-04-19 13:15:49 normalization >         Columns removed: []
2022-04-19 13:15:49 normalization >         Data types changed: []
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.820236 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.820415 (Thread-1): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.889778 (Thread-3): SQL status: SELECT in 0.51 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.897912 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:33.898137 (Thread-3): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.083841 (Thread-4): SQL status: SELECT in 0.34 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.087165 (Thread-4): 
2022-04-19 13:15:49 normalization >     In "datalake"."zendesk"."ticket_metrics_agent_wait_time_in_minutes":
2022-04-19 13:15:49 normalization >         Schema changed: False
2022-04-19 13:15:49 normalization >         Source columns not in target: []
2022-04-19 13:15:49 normalization >         Target columns not in source: []
2022-04-19 13:15:49 normalization >         New column types: []
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.091412 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.091579 (Thread-4): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.244675 (Thread-1): SQL status: SELECT in 0.42 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.247937 (Thread-1): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.248397 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.248555 (Thread-1): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       delete
2022-04-19 13:15:49 normalization >     from "datalake".zendesk."ticket_metrics"
2022-04-19 13:15:49 normalization >     where (_airbyte_unique_key) in (
2022-04-19 13:15:49 normalization >         select (_airbyte_unique_key)
2022-04-19 13:15:49 normalization >         from "ticket_metrics__dbt_tmp131530519919"
2022-04-19 13:15:49 normalization >     );
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     insert into "datalake".zendesk."ticket_metrics" ("id", "url", "time", "type", "metric", "status", "reopens", "replies", "solved_at", "ticket_id", "created_at", "updated_at", "assigned_at", "instance_id", "group_stations", "assignee_stations", "status_updated_at", "assignee_updated_at", "requester_updated_at", "initially_assigned_at", "reply_time_in_minutes", "latest_comment_added_at", "on_hold_time_in_minutes", "agent_wait_time_in_minutes", "requester_wait_time_in_minutes", "full_resolution_time_in_minutes", "first_resolution_time_in_minutes", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_metrics_hashid", "_airbyte_unique_key")
2022-04-19 13:15:49 normalization >     (
2022-04-19 13:15:49 normalization >        select "id", "url", "time", "type", "metric", "status", "reopens", "replies", "solved_at", "ticket_id", "created_at", "updated_at", "assigned_at", "instance_id", "group_stations", "assignee_stations", "status_updated_at", "assignee_updated_at", "requester_updated_at", "initially_assigned_at", "reply_time_in_minutes", "latest_comment_added_at", "on_hold_time_in_minutes", "agent_wait_time_in_minutes", "requester_wait_time_in_minutes", "full_resolution_time_in_minutes", "first_resolution_time_in_minutes", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_metrics_hashid", "_airbyte_unique_key"
2022-04-19 13:15:49 normalization >        from "ticket_metrics__dbt_tmp131530519919"
2022-04-19 13:15:49 normalization >     );
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.263221 (Thread-3): SQL status: SELECT in 0.36 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.268644 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.268821 (Thread-3): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_first_resolution_time_in_mi__dbt_tmp131532099552'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_first_resolution_time_in_mi__dbt_tmp131532099552'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'None'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_first_resolution_time_in_mi__dbt_tmp131532099552'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.554334 (Thread-4): SQL status: SELECT in 0.46 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.556728 (Thread-4): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.557175 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.557316 (Thread-4): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     insert into "datalake".zendesk."ticket_metrics_agent_wait_time_in_minutes" ("_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_agent_wait_time_in_minutes_hashid")
2022-04-19 13:15:49 normalization >     (
2022-04-19 13:15:49 normalization >        select "_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_agent_wait_time_in_minutes_hashid"
2022-04-19 13:15:49 normalization >        from "ticket_metrics_agent_wait_time_in_minutes__dbt_tmp131531026140"
2022-04-19 13:15:49 normalization >     );
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.604772 (Thread-2): SQL status: SELECT in 11.09 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.609297 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.609507 (Thread-2): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_fields__dbt_tmp131523364395'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_fields__dbt_tmp131523364395'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'None'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_fields__dbt_tmp131523364395'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.630533 (Thread-3): SQL status: SELECT in 0.36 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.635521 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.635826 (Thread-3): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.827615 (Thread-2): SQL status: SELECT in 0.22 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.833689 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.833891 (Thread-2): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_fields'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_fields'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_fields'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.857357 (Thread-3): SQL status: SELECT in 0.22 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.860277 (Thread-3): 
2022-04-19 13:15:49 normalization >     In "datalake"."zendesk"."ticket_metrics_first_resolution_time_in_minutes":
2022-04-19 13:15:49 normalization >         Schema changed: False
2022-04-19 13:15:49 normalization >         Source columns not in target: []
2022-04-19 13:15:49 normalization >         Target columns not in source: []
2022-04-19 13:15:49 normalization >         New column types: []
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.863317 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.863471 (Thread-3): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:34.998309 (Thread-1): SQL status: INSERT 0 17020 in 0.75 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.005708 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.005940 (Thread-1): On model.airbyte_utils.ticket_metrics: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.044559 (Thread-2): SQL status: SELECT in 0.21 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.052141 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.052362 (Thread-2): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_fields__dbt_tmp131523364395'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_fields__dbt_tmp131523364395'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'None'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_fields__dbt_tmp131523364395'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.140539 (Thread-4): SQL status: INSERT 0 67745 in 0.58 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.145652 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.145818 (Thread-4): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_agent_wait_time_in_minutes'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.212121 (Thread-3): SQL status: SELECT in 0.35 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.214537 (Thread-3): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.214961 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.215103 (Thread-3): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     insert into "datalake".zendesk."ticket_metrics_first_resolution_time_in_minutes" ("_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_first_resolution_time_in_minutes_hashid")
2022-04-19 13:15:49 normalization >     (
2022-04-19 13:15:49 normalization >        select "_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_first_resolution_time_in_minutes_hashid"
2022-04-19 13:15:49 normalization >        from "ticket_metrics_first_resolution_time_in_mi__dbt_tmp131532099552"
2022-04-19 13:15:49 normalization >     );
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.418179 (Thread-1): SQL status: SELECT in 0.41 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.421720 (Thread-1): On model.airbyte_utils.ticket_metrics: COMMIT
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.421895 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.422035 (Thread-1): On model.airbyte_utils.ticket_metrics: COMMIT
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.433210 (Thread-2): SQL status: SELECT in 0.38 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.438561 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.438730 (Thread-2): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_fields'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_fields'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_fields'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.460437 (Thread-4): SQL status: SELECT in 0.31 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.462572 (Thread-4): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: COMMIT
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.462725 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.462848 (Thread-4): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: COMMIT
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.582135 (Thread-2): SQL status: SELECT in 0.14 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.589606 (Thread-2): 
2022-04-19 13:15:49 normalization >     In "datalake"."zendesk"."ticket_fields":
2022-04-19 13:15:49 normalization >         Schema changed: True
2022-04-19 13:15:49 normalization >         Source columns not in target: [<Column _airbyte_unique_key (character varying(32))>]
2022-04-19 13:15:49 normalization >         Target columns not in source: []
2022-04-19 13:15:49 normalization >         New column types: []
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.590538 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:35.590687 (Thread-2): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     alter table "datalake"."zendesk"."ticket_fields" add column _airbyte_unique_key character varying(32)
2022-04-19 13:15:49 normalization >       
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.067169 (Thread-2): SQL status: ALTER TABLE in 0.48 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.068858 (Thread-2): 
2022-04-19 13:15:49 normalization >     In "datalake"."zendesk"."ticket_fields":
2022-04-19 13:15:49 normalization >         Schema change approach: sync_all_columns
2022-04-19 13:15:49 normalization >         Columns added: [<Column _airbyte_unique_key (character varying(32))>]
2022-04-19 13:15:49 normalization >         Columns removed: []
2022-04-19 13:15:49 normalization >         Data types changed: []
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.072120 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.072297 (Thread-2): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_fields'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_fields'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_fields'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.166343 (Thread-1): SQL status: COMMIT in 0.74 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.167234 (Thread-1): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.167492 (Thread-4): SQL status: COMMIT in 0.70 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.167971 (Thread-1): On model.airbyte_utils.ticket_metrics: Close
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.168608 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.168949 (Thread-4): On model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes: Close
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.169614 (Thread-1): 13:15:36 | 26 of 33 OK created incremental model zendesk.ticket_metrics................................................. [INSERT 0 17020 in 6.19s]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.169991 (Thread-1): Finished running node model.airbyte_utils.ticket_metrics
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.170399 (Thread-1): Began running node model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.171047 (Thread-4): 13:15:36 | 27 of 33 OK created incremental model zendesk.ticket_metrics_agent_wait_time_in_minutes...................... [INSERT 0 67745 in 5.78s]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.171303 (Thread-1): 13:15:36 | 29 of 33 START incremental model zendesk.ticket_metrics_full_resolution_time_in_minutes...................... [RUN]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.171598 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_agent_wait_time_in_minutes
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.172621 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.173249 (Thread-1): Compiling model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.172872 (Thread-4): Began running node model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.187377 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.187714 (Thread-4): 13:15:36 | 30 of 33 START incremental model zendesk.ticket_metrics_on_hold_time_in_minutes.............................. [RUN]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.187920 (Thread-1): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: BEGIN
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.192462 (Thread-4): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.192810 (Thread-4): Compiling model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.192635 (Thread-1): Opening a new connection, currently in state closed
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.203508 (Thread-1): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.203286 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.204011 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: BEGIN
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.204173 (Thread-4): Opening a new connection, currently in state closed
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.204332 (Thread-4): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.222886 (Thread-3): SQL status: INSERT 0 67745 in 1.01 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.227744 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.227911 (Thread-3): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_first_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.281202 (Thread-4): SQL status: BEGIN in 0.08 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.281505 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.281639 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.293232 (Thread-1): SQL status: BEGIN in 0.10 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.293444 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.293577 (Thread-1): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.398139 (Thread-2): SQL status: SELECT in 0.33 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.401283 (Thread-2): Writing runtime SQL for node "model.airbyte_utils.ticket_fields"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.401715 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.401859 (Thread-2): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       delete
2022-04-19 13:15:49 normalization >     from "datalake".zendesk."ticket_fields"
2022-04-19 13:15:49 normalization >     where (_airbyte_unique_key) in (
2022-04-19 13:15:49 normalization >         select (_airbyte_unique_key)
2022-04-19 13:15:49 normalization >         from "ticket_fields__dbt_tmp131523364395"
2022-04-19 13:15:49 normalization >     );
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     insert into "datalake".zendesk."ticket_fields" ("id", "tag", "url", "type", "title", "active", "position", "required", "raw_title", "removable", "created_at", "updated_at", "description", "sub_type_id", "raw_description", "title_in_portal", "agent_description", "visible_in_portal", "editable_in_portal", "required_in_portal", "raw_title_in_portal", "collapsed_for_agents", "custom_field_options", "system_field_options", "regexp_for_validation", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_fields_hashid", "_airbyte_unique_key")
2022-04-19 13:15:49 normalization >     (
2022-04-19 13:15:49 normalization >        select "id", "tag", "url", "type", "title", "active", "position", "required", "raw_title", "removable", "created_at", "updated_at", "description", "sub_type_id", "raw_description", "title_in_portal", "agent_description", "visible_in_portal", "editable_in_portal", "required_in_portal", "raw_title_in_portal", "collapsed_for_agents", "custom_field_options", "system_field_options", "regexp_for_validation", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_ticket_fields_hashid", "_airbyte_unique_key"
2022-04-19 13:15:49 normalization >        from "ticket_fields__dbt_tmp131523364395"
2022-04-19 13:15:49 normalization >     );
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.718110 (Thread-2): SQL status: INSERT 0 14 in 0.32 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.723210 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.723418 (Thread-2): On model.airbyte_utils.ticket_fields: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_fields"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_fields'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_fields'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_fields'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.740777 (Thread-3): SQL status: SELECT in 0.51 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.743204 (Thread-3): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: COMMIT
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.743363 (Thread-1): SQL status: SELECT in 0.45 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.743521 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.751980 (Thread-3): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: COMMIT
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.756636 (Thread-1): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.757156 (Thread-1): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.768645 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.768941 (Thread-1): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.773892 (Thread-4): SQL status: SELECT in 0.49 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.784722 (Thread-4): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.785255 (Thread-4): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.793825 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:36.794002 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:37.045333 (Thread-2): SQL status: SELECT in 0.32 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:37.048322 (Thread-2): On model.airbyte_utils.ticket_fields: COMMIT
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:37.048502 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_fields".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:37.048626 (Thread-2): On model.airbyte_utils.ticket_fields: COMMIT
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:37.076316 (Thread-1): SQL status: SELECT in 0.31 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:37.081666 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:37.081853 (Thread-1): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >   create temporary table
2022-04-19 13:15:49 normalization >     "ticket_metrics_full_resolution_time_in_min__dbt_tmp131536762542"
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >       compound sortkey(_airbyte_emitted_at)
2022-04-19 13:15:49 normalization >   as (
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization > with __dbt__cte__ticket_metrics_full_resolution_time_in_minutes_ab1 as (
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:49 normalization > -- depends_on: "datalake".zendesk."ticket_metrics_scd"
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:49 normalization >     case when json_extract_path_text(full_resolution_time_in_minutes, 'business', true) != '' then json_extract_path_text(full_resolution_time_in_minutes, 'business', true) end as business,
2022-04-19 13:15:49 normalization >     case when json_extract_path_text(full_resolution_time_in_minutes, 'calendar', true) != '' then json_extract_path_text(full_resolution_time_in_minutes, 'calendar', true) end as calendar,
2022-04-19 13:15:49 normalization >     _airbyte_ab_id,
2022-04-19 13:15:49 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:49 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:49 normalization > from "datalake".zendesk."ticket_metrics_scd" as table_alias
2022-04-19 13:15:49 normalization > -- full_resolution_time_in_minutes at ticket_metrics/full_resolution_time_in_minutes
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > and full_resolution_time_in_minutes is not null
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > ),  __dbt__cte__ticket_metrics_full_resolution_time_in_minutes_ab2 as (
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:49 normalization > -- depends_on: __dbt__cte__ticket_metrics_full_resolution_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:49 normalization >     cast(business as 
2022-04-19 13:15:49 normalization >     bigint
2022-04-19 13:15:49 normalization > ) as business,
2022-04-19 13:15:49 normalization >     cast(calendar as 
2022-04-19 13:15:49 normalization >     bigint
2022-04-19 13:15:49 normalization > ) as calendar,
2022-04-19 13:15:49 normalization >     _airbyte_ab_id,
2022-04-19 13:15:49 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:49 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:49 normalization > from __dbt__cte__ticket_metrics_full_resolution_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > -- full_resolution_time_in_minutes at ticket_metrics/full_resolution_time_in_minutes
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > ),  __dbt__cte__ticket_metrics_full_resolution_time_in_minutes_ab3 as (
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > -- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:49 normalization > -- depends_on: __dbt__cte__ticket_metrics_full_resolution_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     md5(cast(coalesce(cast(_airbyte_ticket_metrics_hashid as varchar), '') || '-' || coalesce(cast(business as varchar), '') || '-' || coalesce(cast(calendar as varchar), '') as varchar)) as _airbyte_full_resolution_time_in_minutes_hashid,
2022-04-19 13:15:49 normalization >     tmp.*
2022-04-19 13:15:49 normalization > from __dbt__cte__ticket_metrics_full_resolution_time_in_minutes_ab2 tmp
2022-04-19 13:15:49 normalization > -- full_resolution_time_in_minutes at ticket_metrics/full_resolution_time_in_minutes
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > )-- Final base SQL model
2022-04-19 13:15:49 normalization > -- depends_on: __dbt__cte__ticket_metrics_full_resolution_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:49 normalization >     business,
2022-04-19 13:15:49 normalization >     calendar,
2022-04-19 13:15:49 normalization >     _airbyte_ab_id,
2022-04-19 13:15:49 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:49 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:49 normalization >     _airbyte_full_resolution_time_in_minutes_hashid
2022-04-19 13:15:49 normalization > from __dbt__cte__ticket_metrics_full_resolution_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > -- full_resolution_time_in_minutes at ticket_metrics/full_resolution_time_in_minutes from "datalake".zendesk."ticket_metrics_scd"
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > and coalesce(
2022-04-19 13:15:49 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:49 normalization >     timestamp with time zone
2022-04-19 13:15:49 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:49 normalization >     timestamp with time zone
2022-04-19 13:15:49 normalization > )) from "datalake".zendesk."ticket_metrics_full_resolution_time_in_minutes"),
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     true)
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >   );
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:37.105871 (Thread-4): SQL status: SELECT in 0.31 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:37.111786 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:37.111994 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >   create temporary table
2022-04-19 13:15:49 normalization >     "ticket_metrics_on_hold_time_in_minutes__dbt_tmp131536790465"
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >       compound sortkey(_airbyte_emitted_at)
2022-04-19 13:15:49 normalization >   as (
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization > with __dbt__cte__ticket_metrics_on_hold_time_in_minutes_ab1 as (
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:49 normalization > -- depends_on: "datalake".zendesk."ticket_metrics_scd"
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:49 normalization >     case when json_extract_path_text(on_hold_time_in_minutes, 'business', true) != '' then json_extract_path_text(on_hold_time_in_minutes, 'business', true) end as business,
2022-04-19 13:15:49 normalization >     case when json_extract_path_text(on_hold_time_in_minutes, 'calendar', true) != '' then json_extract_path_text(on_hold_time_in_minutes, 'calendar', true) end as calendar,
2022-04-19 13:15:49 normalization >     _airbyte_ab_id,
2022-04-19 13:15:49 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:49 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:49 normalization > from "datalake".zendesk."ticket_metrics_scd" as table_alias
2022-04-19 13:15:49 normalization > -- on_hold_time_in_minutes at ticket_metrics/on_hold_time_in_minutes
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > and on_hold_time_in_minutes is not null
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > ),  __dbt__cte__ticket_metrics_on_hold_time_in_minutes_ab2 as (
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:49 normalization > -- depends_on: __dbt__cte__ticket_metrics_on_hold_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:49 normalization >     cast(business as 
2022-04-19 13:15:49 normalization >     bigint
2022-04-19 13:15:49 normalization > ) as business,
2022-04-19 13:15:49 normalization >     cast(calendar as 
2022-04-19 13:15:49 normalization >     bigint
2022-04-19 13:15:49 normalization > ) as calendar,
2022-04-19 13:15:49 normalization >     _airbyte_ab_id,
2022-04-19 13:15:49 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:49 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:49 normalization > from __dbt__cte__ticket_metrics_on_hold_time_in_minutes_ab1
2022-04-19 13:15:49 normalization > -- on_hold_time_in_minutes at ticket_metrics/on_hold_time_in_minutes
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > ),  __dbt__cte__ticket_metrics_on_hold_time_in_minutes_ab3 as (
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > -- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:49 normalization > -- depends_on: __dbt__cte__ticket_metrics_on_hold_time_in_minutes_ab2
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     md5(cast(coalesce(cast(_airbyte_ticket_metrics_hashid as varchar), '') || '-' || coalesce(cast(business as varchar), '') || '-' || coalesce(cast(calendar as varchar), '') as varchar)) as _airbyte_on_hold_time_in_minutes_hashid,
2022-04-19 13:15:49 normalization >     tmp.*
2022-04-19 13:15:49 normalization > from __dbt__cte__ticket_metrics_on_hold_time_in_minutes_ab2 tmp
2022-04-19 13:15:49 normalization > -- on_hold_time_in_minutes at ticket_metrics/on_hold_time_in_minutes
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > )-- Final base SQL model
2022-04-19 13:15:49 normalization > -- depends_on: __dbt__cte__ticket_metrics_on_hold_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > select
2022-04-19 13:15:49 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:49 normalization >     business,
2022-04-19 13:15:49 normalization >     calendar,
2022-04-19 13:15:49 normalization >     _airbyte_ab_id,
2022-04-19 13:15:49 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:49 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:49 normalization >     _airbyte_on_hold_time_in_minutes_hashid
2022-04-19 13:15:49 normalization > from __dbt__cte__ticket_metrics_on_hold_time_in_minutes_ab3
2022-04-19 13:15:49 normalization > -- on_hold_time_in_minutes at ticket_metrics/on_hold_time_in_minutes from "datalake".zendesk."ticket_metrics_scd"
2022-04-19 13:15:49 normalization > where 1 = 1
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization > and coalesce(
2022-04-19 13:15:49 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:49 normalization >     timestamp with time zone
2022-04-19 13:15:49 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:49 normalization >     timestamp with time zone
2022-04-19 13:15:49 normalization > )) from "datalake".zendesk."ticket_metrics_on_hold_time_in_minutes"),
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     true)
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >   );
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.233993 (Thread-3): SQL status: COMMIT in 1.48 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.234868 (Thread-3): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.235094 (Thread-3): On model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes: Close
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.235873 (Thread-3): 13:15:38 | 28 of 33 OK created incremental model zendesk.ticket_metrics_first_resolution_time_in_minutes................ [INSERT 0 67745 in 6.54s]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.236346 (Thread-2): SQL status: COMMIT in 1.19 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.237038 (Thread-2): finished collecting timing info
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.237224 (Thread-2): On model.airbyte_utils.ticket_fields: Close
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.237859 (Thread-2): 13:15:38 | 17 of 33 OK created incremental model zendesk.ticket_fields.................................................. [INSERT 0 14 in 15.42s]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.238048 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_first_resolution_time_in_minutes
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.238259 (Thread-3): Began running node model.airbyte_utils.ticket_metrics_reply_time_in_minutes
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.239077 (Thread-3): 13:15:38 | 31 of 33 START incremental model zendesk.ticket_metrics_reply_time_in_minutes................................ [RUN]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.238665 (Thread-2): Finished running node model.airbyte_utils.ticket_fields
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.239520 (Thread-2): Began running node model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.239830 (Thread-2): 13:15:38 | 32 of 33 START incremental model zendesk.ticket_metrics_requester_wait_time_in_minutes....................... [RUN]
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.240236 (Thread-3): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.240431 (Thread-3): Compiling model.airbyte_utils.ticket_metrics_reply_time_in_minutes
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.248739 (Thread-2): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.248929 (Thread-2): Compiling model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.292056 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.292457 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: BEGIN
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.272366 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.295293 (Thread-2): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: BEGIN
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.296787 (Thread-2): Opening a new connection, currently in state closed
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.297010 (Thread-2): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.293875 (Thread-3): Opening a new connection, currently in state closed
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.297482 (Thread-3): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.329088 (Thread-3): SQL status: BEGIN in 0.04 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.329367 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.329498 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_reply_time_in_minutes'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.330608 (Thread-2): SQL status: BEGIN in 0.03 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.330756 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.330870 (Thread-2): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization >       from
2022-04-19 13:15:49 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:49 normalization >       where
2022-04-19 13:15:49 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:49 normalization >         and tablename = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unioned as (
2022-04-19 13:15:49 normalization >       select * from bound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from unbound_views
2022-04-19 13:15:49 normalization >       union all
2022-04-19 13:15:49 normalization >       select * from external_views
2022-04-19 13:15:49 normalization >     )
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     select
2022-04-19 13:15:49 normalization >       column_name,
2022-04-19 13:15:49 normalization >       data_type,
2022-04-19 13:15:49 normalization >       character_maximum_length,
2022-04-19 13:15:49 normalization >       numeric_precision,
2022-04-19 13:15:49 normalization >       numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     from unioned
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:49 normalization >     
2022-04-19 13:15:49 normalization >     order by ordinal_position
2022-04-19 13:15:49 normalization >   
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.620926 (Thread-4): SQL status: SELECT in 1.51 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.625381 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.625572 (Thread-1): SQL status: SELECT in 1.54 seconds
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.632274 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-19 13:15:49 normalization > 2022-04-19 13:15:38.632460 (Thread-1): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       with bound_views as (
2022-04-19 13:15:49 normalization >         select
2022-04-19 13:15:49 normalization >           ordinal_position,
2022-04-19 13:15:49 normalization >           table_schema,
2022-04-19 13:15:49 normalization >           column_name,
2022-04-19 13:15:49 normalization >           data_type,
2022-04-19 13:15:49 normalization >           character_maximum_length,
2022-04-19 13:15:49 normalization >           numeric_precision,
2022-04-19 13:15:49 normalization >           numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >         from information_schema."columns"
2022-04-19 13:15:49 normalization >         where table_name = 'ticket_metrics_full_resolution_time_in_min__dbt_tmp131536762542'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     unbound_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         ordinal_position,
2022-04-19 13:15:49 normalization >         view_schema,
2022-04-19 13:15:49 normalization >         col_name,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:49 normalization >             'character varying'
2022-04-19 13:15:49 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else col_type
2022-04-19 13:15:49 normalization >         end as col_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'character%'
2022-04-19 13:15:49 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_precision,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as numeric_scale
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:49 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:49 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:49 normalization >       where view_name = 'ticket_metrics_full_resolution_time_in_min__dbt_tmp131536762542'
2022-04-19 13:15:49 normalization >     ),
2022-04-19 13:15:49 normalization > 
2022-04-19 13:15:49 normalization >     external_views as (
2022-04-19 13:15:49 normalization >       select
2022-04-19 13:15:49 normalization >         columnnum,
2022-04-19 13:15:49 normalization >         schemaname,
2022-04-19 13:15:49 normalization >         columnname,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:49 normalization >           then 'character varying'
2022-04-19 13:15:49 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:49 normalization >           else external_type
2022-04-19 13:15:49 normalization >         end as external_type,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:49 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:49 normalization >             '')::int
2022-04-19 13:15:49 normalization >           else null
2022-04-19 13:15:49 normalization >         end as character_maximum_length,
2022-04-19 13:15:49 normalization >         case
2022-04-19 13:15:49 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:49 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'None'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_full_resolution_time_in_min__dbt_tmp131536762542'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:38.625747 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_on_hold_time_in_minutes__dbt_tmp131536790465'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_on_hold_time_in_minutes__dbt_tmp131536790465'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'None'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_on_hold_time_in_minutes__dbt_tmp131536790465'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:38.780422 (Thread-2): SQL status: SELECT in 0.45 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:38.790552 (Thread-2): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:38.790926 (Thread-2): finished collecting timing info
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:38.800595 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:38.800761 (Thread-2): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:38.816105 (Thread-3): SQL status: SELECT in 0.49 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:38.826025 (Thread-3): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:38.826375 (Thread-3): finished collecting timing info
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:38.834607 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:38.834768 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_reply_time_in_minutes'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.093253 (Thread-4): SQL status: SELECT in 0.46 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.098303 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.098467 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.115787 (Thread-1): SQL status: SELECT in 0.48 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.120104 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.120302 (Thread-1): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.219232 (Thread-2): SQL status: SELECT in 0.42 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.224523 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.224707 (Thread-2): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >   create temporary table
2022-04-19 13:15:50 normalization >     "ticket_metrics_requester_wait_time_in_minu__dbt_tmp131538796802"
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >       compound sortkey(_airbyte_emitted_at)
2022-04-19 13:15:50 normalization >   as (
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization > with __dbt__cte__ticket_metrics_requester_wait_time_in_minutes_ab1 as (
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:50 normalization > -- depends_on: "datalake".zendesk."ticket_metrics_scd"
2022-04-19 13:15:50 normalization > select
2022-04-19 13:15:50 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:50 normalization >     case when json_extract_path_text(requester_wait_time_in_minutes, 'business', true) != '' then json_extract_path_text(requester_wait_time_in_minutes, 'business', true) end as business,
2022-04-19 13:15:50 normalization >     case when json_extract_path_text(requester_wait_time_in_minutes, 'calendar', true) != '' then json_extract_path_text(requester_wait_time_in_minutes, 'calendar', true) end as calendar,
2022-04-19 13:15:50 normalization >     _airbyte_ab_id,
2022-04-19 13:15:50 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:50 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:50 normalization > from "datalake".zendesk."ticket_metrics_scd" as table_alias
2022-04-19 13:15:50 normalization > -- requester_wait_time_in_minutes at ticket_metrics/requester_wait_time_in_minutes
2022-04-19 13:15:50 normalization > where 1 = 1
2022-04-19 13:15:50 normalization > and requester_wait_time_in_minutes is not null
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > ),  __dbt__cte__ticket_metrics_requester_wait_time_in_minutes_ab2 as (
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:50 normalization > -- depends_on: __dbt__cte__ticket_metrics_requester_wait_time_in_minutes_ab1
2022-04-19 13:15:50 normalization > select
2022-04-19 13:15:50 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:50 normalization >     cast(business as 
2022-04-19 13:15:50 normalization >     bigint
2022-04-19 13:15:50 normalization > ) as business,
2022-04-19 13:15:50 normalization >     cast(calendar as 
2022-04-19 13:15:50 normalization >     bigint
2022-04-19 13:15:50 normalization > ) as calendar,
2022-04-19 13:15:50 normalization >     _airbyte_ab_id,
2022-04-19 13:15:50 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:50 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:50 normalization > from __dbt__cte__ticket_metrics_requester_wait_time_in_minutes_ab1
2022-04-19 13:15:50 normalization > -- requester_wait_time_in_minutes at ticket_metrics/requester_wait_time_in_minutes
2022-04-19 13:15:50 normalization > where 1 = 1
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > ),  __dbt__cte__ticket_metrics_requester_wait_time_in_minutes_ab3 as (
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > -- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:50 normalization > -- depends_on: __dbt__cte__ticket_metrics_requester_wait_time_in_minutes_ab2
2022-04-19 13:15:50 normalization > select
2022-04-19 13:15:50 normalization >     md5(cast(coalesce(cast(_airbyte_ticket_metrics_hashid as varchar), '') || '-' || coalesce(cast(business as varchar), '') || '-' || coalesce(cast(calendar as varchar), '') as varchar)) as _airbyte_requester_wait_time_in_minutes_hashid,
2022-04-19 13:15:50 normalization >     tmp.*
2022-04-19 13:15:50 normalization > from __dbt__cte__ticket_metrics_requester_wait_time_in_minutes_ab2 tmp
2022-04-19 13:15:50 normalization > -- requester_wait_time_in_minutes at ticket_metrics/requester_wait_time_in_minutes
2022-04-19 13:15:50 normalization > where 1 = 1
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > )-- Final base SQL model
2022-04-19 13:15:50 normalization > -- depends_on: __dbt__cte__ticket_metrics_requester_wait_time_in_minutes_ab3
2022-04-19 13:15:50 normalization > select
2022-04-19 13:15:50 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:50 normalization >     business,
2022-04-19 13:15:50 normalization >     calendar,
2022-04-19 13:15:50 normalization >     _airbyte_ab_id,
2022-04-19 13:15:50 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:50 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:50 normalization >     _airbyte_requester_wait_time_in_minutes_hashid
2022-04-19 13:15:50 normalization > from __dbt__cte__ticket_metrics_requester_wait_time_in_minutes_ab3
2022-04-19 13:15:50 normalization > -- requester_wait_time_in_minutes at ticket_metrics/requester_wait_time_in_minutes from "datalake".zendesk."ticket_metrics_scd"
2022-04-19 13:15:50 normalization > where 1 = 1
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > and coalesce(
2022-04-19 13:15:50 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:50 normalization >     timestamp with time zone
2022-04-19 13:15:50 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:50 normalization >     timestamp with time zone
2022-04-19 13:15:50 normalization > )) from "datalake".zendesk."ticket_metrics_requester_wait_time_in_minutes"),
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     true)
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >   );
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.235491 (Thread-3): SQL status: SELECT in 0.40 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.249200 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.249382 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >   create temporary table
2022-04-19 13:15:50 normalization >     "ticket_metrics_reply_time_in_minutes__dbt_tmp131538830550"
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >       compound sortkey(_airbyte_emitted_at)
2022-04-19 13:15:50 normalization >   as (
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization > with __dbt__cte__ticket_metrics_reply_time_in_minutes_ab1 as (
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:50 normalization > -- depends_on: "datalake".zendesk."ticket_metrics_scd"
2022-04-19 13:15:50 normalization > select
2022-04-19 13:15:50 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:50 normalization >     case when json_extract_path_text(reply_time_in_minutes, 'business', true) != '' then json_extract_path_text(reply_time_in_minutes, 'business', true) end as business,
2022-04-19 13:15:50 normalization >     case when json_extract_path_text(reply_time_in_minutes, 'calendar', true) != '' then json_extract_path_text(reply_time_in_minutes, 'calendar', true) end as calendar,
2022-04-19 13:15:50 normalization >     _airbyte_ab_id,
2022-04-19 13:15:50 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:50 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:50 normalization > from "datalake".zendesk."ticket_metrics_scd" as table_alias
2022-04-19 13:15:50 normalization > -- reply_time_in_minutes at ticket_metrics/reply_time_in_minutes
2022-04-19 13:15:50 normalization > where 1 = 1
2022-04-19 13:15:50 normalization > and reply_time_in_minutes is not null
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > ),  __dbt__cte__ticket_metrics_reply_time_in_minutes_ab2 as (
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:50 normalization > -- depends_on: __dbt__cte__ticket_metrics_reply_time_in_minutes_ab1
2022-04-19 13:15:50 normalization > select
2022-04-19 13:15:50 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:50 normalization >     cast(business as 
2022-04-19 13:15:50 normalization >     bigint
2022-04-19 13:15:50 normalization > ) as business,
2022-04-19 13:15:50 normalization >     cast(calendar as 
2022-04-19 13:15:50 normalization >     bigint
2022-04-19 13:15:50 normalization > ) as calendar,
2022-04-19 13:15:50 normalization >     _airbyte_ab_id,
2022-04-19 13:15:50 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:50 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:50 normalization > from __dbt__cte__ticket_metrics_reply_time_in_minutes_ab1
2022-04-19 13:15:50 normalization > -- reply_time_in_minutes at ticket_metrics/reply_time_in_minutes
2022-04-19 13:15:50 normalization > where 1 = 1
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > ),  __dbt__cte__ticket_metrics_reply_time_in_minutes_ab3 as (
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > -- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:50 normalization > -- depends_on: __dbt__cte__ticket_metrics_reply_time_in_minutes_ab2
2022-04-19 13:15:50 normalization > select
2022-04-19 13:15:50 normalization >     md5(cast(coalesce(cast(_airbyte_ticket_metrics_hashid as varchar), '') || '-' || coalesce(cast(business as varchar), '') || '-' || coalesce(cast(calendar as varchar), '') as varchar)) as _airbyte_reply_time_in_minutes_hashid,
2022-04-19 13:15:50 normalization >     tmp.*
2022-04-19 13:15:50 normalization > from __dbt__cte__ticket_metrics_reply_time_in_minutes_ab2 tmp
2022-04-19 13:15:50 normalization > -- reply_time_in_minutes at ticket_metrics/reply_time_in_minutes
2022-04-19 13:15:50 normalization > where 1 = 1
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > )-- Final base SQL model
2022-04-19 13:15:50 normalization > -- depends_on: __dbt__cte__ticket_metrics_reply_time_in_minutes_ab3
2022-04-19 13:15:50 normalization > select
2022-04-19 13:15:50 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:50 normalization >     business,
2022-04-19 13:15:50 normalization >     calendar,
2022-04-19 13:15:50 normalization >     _airbyte_ab_id,
2022-04-19 13:15:50 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:50 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:50 normalization >     _airbyte_reply_time_in_minutes_hashid
2022-04-19 13:15:50 normalization > from __dbt__cte__ticket_metrics_reply_time_in_minutes_ab3
2022-04-19 13:15:50 normalization > -- reply_time_in_minutes at ticket_metrics/reply_time_in_minutes from "datalake".zendesk."ticket_metrics_scd"
2022-04-19 13:15:50 normalization > where 1 = 1
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > and coalesce(
2022-04-19 13:15:50 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:50 normalization >     timestamp with time zone
2022-04-19 13:15:50 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:50 normalization >     timestamp with time zone
2022-04-19 13:15:50 normalization > )) from "datalake".zendesk."ticket_metrics_reply_time_in_minutes"),
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     true)
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >   );
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.445940 (Thread-4): SQL status: SELECT in 0.35 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.451602 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.451776 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_on_hold_time_in_minutes__dbt_tmp131536790465'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_on_hold_time_in_minutes__dbt_tmp131536790465'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'None'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_on_hold_time_in_minutes__dbt_tmp131536790465'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.475453 (Thread-1): SQL status: SELECT in 0.35 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.480281 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.480444 (Thread-1): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_full_resolution_time_in_min__dbt_tmp131536762542'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_full_resolution_time_in_min__dbt_tmp131536762542'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'None'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_full_resolution_time_in_min__dbt_tmp131536762542'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.661776 (Thread-4): SQL status: SELECT in 0.21 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.666765 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.666958 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.699927 (Thread-1): SQL status: SELECT in 0.22 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.706352 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.706544 (Thread-1): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:39.971330 (Thread-1): SQL status: SELECT in 0.26 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.044301 (Thread-4): SQL status: SELECT in 0.38 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.046051 (Thread-1): 
2022-04-19 13:15:50 normalization >     In "datalake"."zendesk"."ticket_metrics_full_resolution_time_in_minutes":
2022-04-19 13:15:50 normalization >         Schema changed: False
2022-04-19 13:15:50 normalization >         Source columns not in target: []
2022-04-19 13:15:50 normalization >         Target columns not in source: []
2022-04-19 13:15:50 normalization >         New column types: []
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.048625 (Thread-4): 
2022-04-19 13:15:50 normalization >     In "datalake"."zendesk"."ticket_metrics_on_hold_time_in_minutes":
2022-04-19 13:15:50 normalization >         Schema changed: False
2022-04-19 13:15:50 normalization >         Source columns not in target: []
2022-04-19 13:15:50 normalization >         Target columns not in source: []
2022-04-19 13:15:50 normalization >         New column types: []
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.051577 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.054431 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.054673 (Thread-1): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.054875 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.132049 (Thread-3): SQL status: SELECT in 0.88 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.136507 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.136669 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_reply_time_in_minutes__dbt_tmp131538830550'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_reply_time_in_minutes__dbt_tmp131538830550'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'None'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_reply_time_in_minutes__dbt_tmp131538830550'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.243990 (Thread-2): SQL status: SELECT in 1.02 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.248441 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.248600 (Thread-2): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_requester_wait_time_in_minu__dbt_tmp131538796802'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_requester_wait_time_in_minu__dbt_tmp131538796802'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'None'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_requester_wait_time_in_minu__dbt_tmp131538796802'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.549209 (Thread-1): SQL status: SELECT in 0.49 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.551577 (Thread-1): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.551985 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.552121 (Thread-1): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     insert into "datalake".zendesk."ticket_metrics_full_resolution_time_in_minutes" ("_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_full_resolution_time_in_minutes_hashid")
2022-04-19 13:15:50 normalization >     (
2022-04-19 13:15:50 normalization >        select "_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_full_resolution_time_in_minutes_hashid"
2022-04-19 13:15:50 normalization >        from "ticket_metrics_full_resolution_time_in_min__dbt_tmp131536762542"
2022-04-19 13:15:50 normalization >     );
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.604649 (Thread-4): SQL status: SELECT in 0.55 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.606988 (Thread-4): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.607410 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.607559 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     insert into "datalake".zendesk."ticket_metrics_on_hold_time_in_minutes" ("_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_on_hold_time_in_minutes_hashid")
2022-04-19 13:15:50 normalization >     (
2022-04-19 13:15:50 normalization >        select "_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_on_hold_time_in_minutes_hashid"
2022-04-19 13:15:50 normalization >        from "ticket_metrics_on_hold_time_in_minutes__dbt_tmp131536790465"
2022-04-19 13:15:50 normalization >     );
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.634501 (Thread-2): SQL status: SELECT in 0.39 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.639254 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.639411 (Thread-2): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.642682 (Thread-3): SQL status: SELECT in 0.51 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.646651 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.646806 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_reply_time_in_minutes'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.868415 (Thread-2): SQL status: SELECT in 0.23 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.875682 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.875868 (Thread-2): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_requester_wait_time_in_minu__dbt_tmp131538796802'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_requester_wait_time_in_minu__dbt_tmp131538796802'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'None'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_requester_wait_time_in_minu__dbt_tmp131538796802'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.878268 (Thread-1): SQL status: INSERT 0 67745 in 0.33 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.882403 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.882568 (Thread-1): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_full_resolution_time_in_minutes'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.911554 (Thread-3): SQL status: SELECT in 0.26 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.916720 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:40.916889 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_reply_time_in_minutes__dbt_tmp131538830550'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 INFO i.a.w.t.TemporalAttemptExecution(lambda$getWorkerThread$2):158 - Completing future exceptionally...
io.airbyte.workers.WorkerException: Normalization Failed.
	at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:60) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:18) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.temporal.TemporalAttemptExecution.lambda$getWorkerThread$2(TemporalAttemptExecution.java:155) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at java.lang.Thread.run(Thread.java:833) [?:?]
Caused by: io.airbyte.workers.WorkerException: Normalization Failed.
	at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:57) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	... 3 more
	Suppressed: io.airbyte.workers.WorkerException: Normalization process wasn't successful
		at io.airbyte.workers.normalization.DefaultNormalizationRunner.close(DefaultNormalizationRunner.java:159) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:45) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:18) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at io.airbyte.workers.temporal.TemporalAttemptExecution.lambda$getWorkerThread$2(TemporalAttemptExecution.java:155) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at java.lang.Thread.run(Thread.java:833) [?:?]
2022-04-19 13:15:50 INFO i.a.w.t.TemporalAttemptExecution(get):131 - Stopping cancellation check scheduling...
2022-04-19 13:15:50 INFO i.a.w.t.TemporalUtils(withBackgroundHeartbeat):234 - Stopping temporal heartbeating...
2022-04-19 13:15:50 WARN i.t.i.s.POJOActivityTaskHandler(activityFailureToResult):363 - Activity failure. ActivityId=d2373ff5-cd21-38fc-b196-eb00fbdc9cd7, activityType=Normalize, attempt=1
java.lang.RuntimeException: io.temporal.serviceclient.CheckedExceptionWrapper: java.util.concurrent.ExecutionException: io.airbyte.workers.WorkerException: Normalization Failed.
	at io.airbyte.workers.temporal.TemporalUtils.withBackgroundHeartbeat(TemporalUtils.java:232) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.temporal.sync.NormalizationActivityImpl.normalize(NormalizationActivityImpl.java:71) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at jdk.internal.reflect.GeneratedMethodAccessor176.invoke(Unknown Source) ~[?:?]
	at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) ~[?:?]
	at java.lang.reflect.Method.invoke(Method.java:568) ~[?:?]
	at io.temporal.internal.sync.POJOActivityTaskHandler$POJOActivityInboundCallsInterceptor.execute(POJOActivityTaskHandler.java:286) ~[temporal-sdk-1.6.0.jar:?]
	at io.temporal.internal.sync.POJOActivityTaskHandler$POJOActivityImplementation.execute(POJOActivityTaskHandler.java:252) ~[temporal-sdk-1.6.0.jar:?]
	at io.temporal.internal.sync.POJOActivityTaskHandler.handle(POJOActivityTaskHandler.java:209) ~[temporal-sdk-1.6.0.jar:?]
	at io.temporal.internal.worker.ActivityWorker$TaskHandlerImpl.handle(ActivityWorker.java:193) ~[temporal-sdk-1.6.0.jar:?]
	at io.temporal.internal.worker.ActivityWorker$TaskHandlerImpl.handle(ActivityWorker.java:151) ~[temporal-sdk-1.6.0.jar:?]
	at io.temporal.internal.worker.PollTaskExecutor.lambda$process$0(PollTaskExecutor.java:73) ~[temporal-sdk-1.6.0.jar:?]
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136) [?:?]
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635) [?:?]
	at java.lang.Thread.run(Thread.java:833) [?:?]
Caused by: io.temporal.serviceclient.CheckedExceptionWrapper: java.util.concurrent.ExecutionException: io.airbyte.workers.WorkerException: Normalization Failed.
	at io.temporal.serviceclient.CheckedExceptionWrapper.wrap(CheckedExceptionWrapper.java:56) ~[temporal-serviceclient-1.6.0.jar:?]
	at io.temporal.internal.sync.WorkflowInternal.wrap(WorkflowInternal.java:412) ~[temporal-sdk-1.6.0.jar:?]
	at io.temporal.activity.Activity.wrap(Activity.java:51) ~[temporal-sdk-1.6.0.jar:?]
	at io.airbyte.workers.temporal.TemporalAttemptExecution.get(TemporalAttemptExecution.java:135) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.temporal.sync.NormalizationActivityImpl.lambda$normalize$1(NormalizationActivityImpl.java:97) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.temporal.TemporalUtils.withBackgroundHeartbeat(TemporalUtils.java:227) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	... 13 more
Caused by: java.util.concurrent.ExecutionException: io.airbyte.workers.WorkerException: Normalization Failed.
	at java.util.concurrent.CompletableFuture.reportGet(CompletableFuture.java:396) ~[?:?]
	at java.util.concurrent.CompletableFuture.get(CompletableFuture.java:2073) ~[?:?]
	at io.airbyte.workers.temporal.TemporalAttemptExecution.get(TemporalAttemptExecution.java:129) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.temporal.sync.NormalizationActivityImpl.lambda$normalize$1(NormalizationActivityImpl.java:97) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.temporal.TemporalUtils.withBackgroundHeartbeat(TemporalUtils.java:227) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	... 13 more
Caused by: io.airbyte.workers.WorkerException: Normalization Failed.
	at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:60) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:18) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.temporal.TemporalAttemptExecution.lambda$getWorkerThread$2(TemporalAttemptExecution.java:155) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	... 1 more
Caused by: io.airbyte.workers.WorkerException: Normalization Failed.
	at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:57) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:18) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	at io.airbyte.workers.temporal.TemporalAttemptExecution.lambda$getWorkerThread$2(TemporalAttemptExecution.java:155) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
	... 1 more
	Suppressed: io.airbyte.workers.WorkerException: Normalization process wasn't successful
		at io.airbyte.workers.normalization.DefaultNormalizationRunner.close(DefaultNormalizationRunner.java:159) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:45) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at io.airbyte.workers.DefaultNormalizationWorker.run(DefaultNormalizationWorker.java:18) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at io.airbyte.workers.temporal.TemporalAttemptExecution.lambda$getWorkerThread$2(TemporalAttemptExecution.java:155) ~[io.airbyte-airbyte-workers-0.35.30-alpha.jar:?]
		at java.lang.Thread.run(Thread.java:833) [?:?]
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_reply_time_in_minutes__dbt_tmp131538830550'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'None'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_reply_time_in_minutes__dbt_tmp131538830550'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.095556 (Thread-4): SQL status: INSERT 0 67745 in 0.49 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.100507 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.100707 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_on_hold_time_in_minutes'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.215972 (Thread-2): SQL status: SELECT in 0.34 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.220976 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.221162 (Thread-2): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.311543 (Thread-1): SQL status: SELECT in 0.43 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.313880 (Thread-1): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: COMMIT
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.314046 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.314170 (Thread-1): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: COMMIT
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.328768 (Thread-3): SQL status: SELECT in 0.41 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.335765 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.335932 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_reply_time_in_minutes'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.386074 (Thread-1): SQL status: COMMIT in 0.07 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.386940 (Thread-1): finished collecting timing info
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.387151 (Thread-1): On model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes: Close
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.387952 (Thread-1): 13:15:41 | 29 of 33 OK created incremental model zendesk.ticket_metrics_full_resolution_time_in_minutes................. [INSERT 0 67745 in 5.22s]
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.388226 (Thread-1): Finished running node model.airbyte_utils.ticket_metrics_full_resolution_time_in_minutes
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.388561 (Thread-1): Began running node model.airbyte_utils.ticket_metrics_status
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.388960 (Thread-1): 13:15:41 | 33 of 33 START incremental model zendesk.ticket_metrics_status............................................... [RUN]
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.389438 (Thread-1): Acquiring new redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.389612 (Thread-1): Compiling model.airbyte_utils.ticket_metrics_status
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.423007 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.423253 (Thread-1): On model.airbyte_utils.ticket_metrics_status: BEGIN
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.423403 (Thread-1): Opening a new connection, currently in state closed
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.423524 (Thread-1): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.494057 (Thread-1): SQL status: BEGIN in 0.07 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.494351 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.494486 (Thread-1): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_status'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_status'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_status'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.508622 (Thread-4): SQL status: SELECT in 0.41 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.510821 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: COMMIT
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.510977 (Thread-4): Using redshift connection "model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.511106 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: COMMIT
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.596698 (Thread-4): SQL status: COMMIT in 0.09 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.597566 (Thread-4): finished collecting timing info
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.597775 (Thread-4): On model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes: Close
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.598588 (Thread-4): 13:15:41 | 30 of 33 OK created incremental model zendesk.ticket_metrics_on_hold_time_in_minutes......................... [INSERT 0 67745 in 5.41s]
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.598994 (Thread-4): Finished running node model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.612180 (Thread-2): SQL status: SELECT in 0.39 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.615108 (Thread-2): 
2022-04-19 13:15:50 normalization >     In "datalake"."zendesk"."ticket_metrics_requester_wait_time_in_minutes":
2022-04-19 13:15:50 normalization >         Schema changed: False
2022-04-19 13:15:50 normalization >         Source columns not in target: []
2022-04-19 13:15:50 normalization >         Target columns not in source: []
2022-04-19 13:15:50 normalization >         New column types: []
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.618548 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.618704 (Thread-2): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.763569 (Thread-3): SQL status: SELECT in 0.43 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.766572 (Thread-3): 
2022-04-19 13:15:50 normalization >     In "datalake"."zendesk"."ticket_metrics_reply_time_in_minutes":
2022-04-19 13:15:50 normalization >         Schema changed: False
2022-04-19 13:15:50 normalization >         Source columns not in target: []
2022-04-19 13:15:50 normalization >         Target columns not in source: []
2022-04-19 13:15:50 normalization >         New column types: []
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.770310 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.770492 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_reply_time_in_minutes'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.967816 (Thread-2): SQL status: SELECT in 0.35 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.970599 (Thread-2): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.970815 (Thread-1): SQL status: SELECT in 0.48 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.981074 (Thread-1): Writing injected SQL for node "model.airbyte_utils.ticket_metrics_status"
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.981458 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.981685 (Thread-2): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     insert into "datalake".zendesk."ticket_metrics_requester_wait_time_in_minutes" ("_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_requester_wait_time_in_minutes_hashid")
2022-04-19 13:15:50 normalization >     (
2022-04-19 13:15:50 normalization >        select "_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_requester_wait_time_in_minutes_hashid"
2022-04-19 13:15:50 normalization >        from "ticket_metrics_requester_wait_time_in_minu__dbt_tmp131538796802"
2022-04-19 13:15:50 normalization >     );
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.982039 (Thread-1): finished collecting timing info
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.989638 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:41.989802 (Thread-1): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_status'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_status'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_status'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.048413 (Thread-3): SQL status: SELECT in 0.28 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.050856 (Thread-3): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.051278 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.051420 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     insert into "datalake".zendesk."ticket_metrics_reply_time_in_minutes" ("_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_reply_time_in_minutes_hashid")
2022-04-19 13:15:50 normalization >     (
2022-04-19 13:15:50 normalization >        select "_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_reply_time_in_minutes_hashid"
2022-04-19 13:15:50 normalization >        from "ticket_metrics_reply_time_in_minutes__dbt_tmp131538830550"
2022-04-19 13:15:50 normalization >     );
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.172458 (Thread-1): SQL status: SELECT in 0.18 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.177740 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.177926 (Thread-1): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >   create temporary table
2022-04-19 13:15:50 normalization >     "ticket_metrics_status__dbt_tmp131541986583"
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >       compound sortkey(_airbyte_emitted_at)
2022-04-19 13:15:50 normalization >   as (
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization > with __dbt__cte__ticket_metrics_status_ab1 as (
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > -- SQL model to parse JSON blob stored in a single column and extract into separated field columns as described by the JSON Schema
2022-04-19 13:15:50 normalization > -- depends_on: "datalake".zendesk."ticket_metrics_scd"
2022-04-19 13:15:50 normalization > select
2022-04-19 13:15:50 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:50 normalization >     case when json_extract_path_text(status, 'business', true) != '' then json_extract_path_text(status, 'business', true) end as business,
2022-04-19 13:15:50 normalization >     case when json_extract_path_text(status, 'calendar', true) != '' then json_extract_path_text(status, 'calendar', true) end as calendar,
2022-04-19 13:15:50 normalization >     _airbyte_ab_id,
2022-04-19 13:15:50 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:50 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:50 normalization > from "datalake".zendesk."ticket_metrics_scd" as table_alias
2022-04-19 13:15:50 normalization > -- status at ticket_metrics/status
2022-04-19 13:15:50 normalization > where 1 = 1
2022-04-19 13:15:50 normalization > and status is not null
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > ),  __dbt__cte__ticket_metrics_status_ab2 as (
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > -- SQL model to cast each column to its adequate SQL type converted from the JSON schema type
2022-04-19 13:15:50 normalization > -- depends_on: __dbt__cte__ticket_metrics_status_ab1
2022-04-19 13:15:50 normalization > select
2022-04-19 13:15:50 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:50 normalization >     cast(business as 
2022-04-19 13:15:50 normalization >     bigint
2022-04-19 13:15:50 normalization > ) as business,
2022-04-19 13:15:50 normalization >     cast(calendar as 
2022-04-19 13:15:50 normalization >     bigint
2022-04-19 13:15:50 normalization > ) as calendar,
2022-04-19 13:15:50 normalization >     _airbyte_ab_id,
2022-04-19 13:15:50 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:50 normalization >     getdate() as _airbyte_normalized_at
2022-04-19 13:15:50 normalization > from __dbt__cte__ticket_metrics_status_ab1
2022-04-19 13:15:50 normalization > -- status at ticket_metrics/status
2022-04-19 13:15:50 normalization > where 1 = 1
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > ),  __dbt__cte__ticket_metrics_status_ab3 as (
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > -- SQL model to build a hash column based on the values of this record
2022-04-19 13:15:50 normalization > -- depends_on: __dbt__cte__ticket_metrics_status_ab2
2022-04-19 13:15:50 normalization > select
2022-04-19 13:15:50 normalization >     md5(cast(coalesce(cast(_airbyte_ticket_metrics_hashid as varchar), '') || '-' || coalesce(cast(business as varchar), '') || '-' || coalesce(cast(calendar as varchar), '') as varchar)) as _airbyte_status_hashid,
2022-04-19 13:15:50 normalization >     tmp.*
2022-04-19 13:15:50 normalization > from __dbt__cte__ticket_metrics_status_ab2 tmp
2022-04-19 13:15:50 normalization > -- status at ticket_metrics/status
2022-04-19 13:15:50 normalization > where 1 = 1
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > )-- Final base SQL model
2022-04-19 13:15:50 normalization > -- depends_on: __dbt__cte__ticket_metrics_status_ab3
2022-04-19 13:15:50 normalization > select
2022-04-19 13:15:50 normalization >     _airbyte_ticket_metrics_hashid,
2022-04-19 13:15:50 normalization >     business,
2022-04-19 13:15:50 normalization >     calendar,
2022-04-19 13:15:50 normalization >     _airbyte_ab_id,
2022-04-19 13:15:50 normalization >     _airbyte_emitted_at,
2022-04-19 13:15:50 normalization >     getdate() as _airbyte_normalized_at,
2022-04-19 13:15:50 normalization >     _airbyte_status_hashid
2022-04-19 13:15:50 normalization > from __dbt__cte__ticket_metrics_status_ab3
2022-04-19 13:15:50 normalization > -- status at ticket_metrics/status from "datalake".zendesk."ticket_metrics_scd"
2022-04-19 13:15:50 normalization > where 1 = 1
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization > and coalesce(
2022-04-19 13:15:50 normalization >     cast(_airbyte_emitted_at as 
2022-04-19 13:15:50 normalization >     timestamp with time zone
2022-04-19 13:15:50 normalization > ) >= (select max(cast(_airbyte_emitted_at as 
2022-04-19 13:15:50 normalization >     timestamp with time zone
2022-04-19 13:15:50 normalization > )) from "datalake".zendesk."ticket_metrics_status"),
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     true)
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >   );
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.253269 (Thread-2): SQL status: INSERT 0 67745 in 0.27 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.261980 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.262229 (Thread-2): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_requester_wait_time_in_minutes'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.398645 (Thread-2): SQL status: SELECT in 0.14 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.401031 (Thread-2): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: COMMIT
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.401206 (Thread-2): Using redshift connection "model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.401334 (Thread-2): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: COMMIT
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.605160 (Thread-3): SQL status: INSERT 0 67745 in 0.55 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.610272 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.610473 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_reply_time_in_minutes"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_reply_time_in_minutes'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_reply_time_in_minutes'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.612186 (Thread-2): SQL status: COMMIT in 0.21 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.612844 (Thread-2): finished collecting timing info
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.613055 (Thread-2): On model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes: Close
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.613837 (Thread-2): 13:15:42 | 32 of 33 OK created incremental model zendesk.ticket_metrics_requester_wait_time_in_minutes.................. [INSERT 0 67745 in 4.37s]
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.614353 (Thread-2): Finished running node model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.630406 (Thread-1): SQL status: SELECT in 0.45 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.635602 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.635834 (Thread-1): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_status__dbt_tmp131541986583'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_status__dbt_tmp131541986583'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'None'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_status__dbt_tmp131541986583'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.841455 (Thread-3): SQL status: SELECT in 0.23 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.845774 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: COMMIT
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.845957 (Thread-3): Using redshift connection "model.airbyte_utils.ticket_metrics_reply_time_in_minutes".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.846101 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: COMMIT
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.869049 (Thread-1): SQL status: SELECT in 0.23 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.874088 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:42.874250 (Thread-1): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_status'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_status'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_status'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.003456 (Thread-1): SQL status: SELECT in 0.13 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.008874 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.009060 (Thread-1): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_status__dbt_tmp131541986583'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_status__dbt_tmp131541986583'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'None'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_status__dbt_tmp131541986583'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.063705 (Thread-3): SQL status: COMMIT in 0.22 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.064590 (Thread-3): finished collecting timing info
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.064813 (Thread-3): On model.airbyte_utils.ticket_metrics_reply_time_in_minutes: Close
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.065589 (Thread-3): 13:15:43 | 31 of 33 OK created incremental model zendesk.ticket_metrics_reply_time_in_minutes........................... [INSERT 0 67745 in 4.83s]
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.066085 (Thread-3): Finished running node model.airbyte_utils.ticket_metrics_reply_time_in_minutes
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.155821 (Thread-1): SQL status: SELECT in 0.15 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.161041 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.161206 (Thread-1): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_status'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_status'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_status'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.290152 (Thread-1): SQL status: SELECT in 0.13 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.293112 (Thread-1): 
2022-04-19 13:15:50 normalization >     In "datalake"."zendesk"."ticket_metrics_status":
2022-04-19 13:15:50 normalization >         Schema changed: False
2022-04-19 13:15:50 normalization >         Source columns not in target: []
2022-04-19 13:15:50 normalization >         Target columns not in source: []
2022-04-19 13:15:50 normalization >         New column types: []
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.296134 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.297165 (Thread-1): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_status'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_status'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_status'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.425649 (Thread-1): SQL status: SELECT in 0.13 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.428510 (Thread-1): Writing runtime SQL for node "model.airbyte_utils.ticket_metrics_status"
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.428995 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.429139 (Thread-1): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     insert into "datalake".zendesk."ticket_metrics_status" ("_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_status_hashid")
2022-04-19 13:15:50 normalization >     (
2022-04-19 13:15:50 normalization >        select "_airbyte_ticket_metrics_hashid", "business", "calendar", "_airbyte_ab_id", "_airbyte_emitted_at", "_airbyte_normalized_at", "_airbyte_status_hashid"
2022-04-19 13:15:50 normalization >        from "ticket_metrics_status__dbt_tmp131541986583"
2022-04-19 13:15:50 normalization >     );
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.464518 (Thread-1): SQL status: INSERT 0 0 in 0.04 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.471674 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.471853 (Thread-1): On model.airbyte_utils.ticket_metrics_status: /* {"app": "dbt", "dbt_version": "0.21.1", "profile_name": "normalize", "target_name": "prod", "node_id": "model.airbyte_utils.ticket_metrics_status"} */
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       with bound_views as (
2022-04-19 13:15:50 normalization >         select
2022-04-19 13:15:50 normalization >           ordinal_position,
2022-04-19 13:15:50 normalization >           table_schema,
2022-04-19 13:15:50 normalization >           column_name,
2022-04-19 13:15:50 normalization >           data_type,
2022-04-19 13:15:50 normalization >           character_maximum_length,
2022-04-19 13:15:50 normalization >           numeric_precision,
2022-04-19 13:15:50 normalization >           numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >         from information_schema."columns"
2022-04-19 13:15:50 normalization >         where table_name = 'ticket_metrics_status'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unbound_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         ordinal_position,
2022-04-19 13:15:50 normalization >         view_schema,
2022-04-19 13:15:50 normalization >         col_name,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type ilike 'character varying%' then
2022-04-19 13:15:50 normalization >             'character varying'
2022-04-19 13:15:50 normalization >           when col_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else col_type
2022-04-19 13:15:50 normalization >         end as col_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'character%'
2022-04-19 13:15:50 normalization >           then nullif(REGEXP_SUBSTR(col_type, '[0-9]+'), '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when col_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(col_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >       from pg_get_late_binding_view_cols()
2022-04-19 13:15:50 normalization >       cols(view_schema name, view_name name, col_name name,
2022-04-19 13:15:50 normalization >            col_type varchar, ordinal_position int)
2022-04-19 13:15:50 normalization >       where view_name = 'ticket_metrics_status'
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     external_views as (
2022-04-19 13:15:50 normalization >       select
2022-04-19 13:15:50 normalization >         columnnum,
2022-04-19 13:15:50 normalization >         schemaname,
2022-04-19 13:15:50 normalization >         columnname,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type ilike 'character varying%' or external_type ilike 'varchar%'
2022-04-19 13:15:50 normalization >           then 'character varying'
2022-04-19 13:15:50 normalization >           when external_type ilike 'numeric%' then 'numeric'
2022-04-19 13:15:50 normalization >           else external_type
2022-04-19 13:15:50 normalization >         end as external_type,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'character%' or external_type like 'varchar%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             REGEXP_SUBSTR(external_type, '[0-9]+'),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as character_maximum_length,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 1),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_precision,
2022-04-19 13:15:50 normalization >         case
2022-04-19 13:15:50 normalization >           when external_type like 'numeric%'
2022-04-19 13:15:50 normalization >           then nullif(
2022-04-19 13:15:50 normalization >             SPLIT_PART(REGEXP_SUBSTR(external_type, '[0-9,]+'), ',', 2),
2022-04-19 13:15:50 normalization >             '')::int
2022-04-19 13:15:50 normalization >           else null
2022-04-19 13:15:50 normalization >         end as numeric_scale
2022-04-19 13:15:50 normalization >       from
2022-04-19 13:15:50 normalization >         pg_catalog.svv_external_columns
2022-04-19 13:15:50 normalization >       where
2022-04-19 13:15:50 normalization >         schemaname = 'zendesk'
2022-04-19 13:15:50 normalization >         and tablename = 'ticket_metrics_status'
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     ),
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     unioned as (
2022-04-19 13:15:50 normalization >       select * from bound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from unbound_views
2022-04-19 13:15:50 normalization >       union all
2022-04-19 13:15:50 normalization >       select * from external_views
2022-04-19 13:15:50 normalization >     )
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     select
2022-04-19 13:15:50 normalization >       column_name,
2022-04-19 13:15:50 normalization >       data_type,
2022-04-19 13:15:50 normalization >       character_maximum_length,
2022-04-19 13:15:50 normalization >       numeric_precision,
2022-04-19 13:15:50 normalization >       numeric_scale
2022-04-19 13:15:50 normalization > 
2022-04-19 13:15:50 normalization >     from unioned
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     where table_schema = 'zendesk'
2022-04-19 13:15:50 normalization >     
2022-04-19 13:15:50 normalization >     order by ordinal_position
2022-04-19 13:15:50 normalization >   
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.612444 (Thread-1): SQL status: SELECT in 0.14 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.614804 (Thread-1): On model.airbyte_utils.ticket_metrics_status: COMMIT
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.614967 (Thread-1): Using redshift connection "model.airbyte_utils.ticket_metrics_status".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.615096 (Thread-1): On model.airbyte_utils.ticket_metrics_status: COMMIT
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.676078 (Thread-1): SQL status: COMMIT in 0.06 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.676994 (Thread-1): finished collecting timing info
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.677207 (Thread-1): On model.airbyte_utils.ticket_metrics_status: Close
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.678007 (Thread-1): 13:15:43 | 33 of 33 OK created incremental model zendesk.ticket_metrics_status.......................................... [INSERT 0 0 in 2.29s]
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.678336 (Thread-1): Finished running node model.airbyte_utils.ticket_metrics_status
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.680229 (MainThread): Acquiring new redshift connection "master".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.680520 (MainThread): Using redshift connection "master".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.680656 (MainThread): On master: BEGIN
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.680785 (MainThread): Opening a new connection, currently in state closed
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.680894 (MainThread): Connecting to Redshift using 'database' credentials
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.711868 (MainThread): SQL status: BEGIN in 0.03 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.712156 (MainThread): On master: COMMIT
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.712312 (MainThread): Using redshift connection "master".
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.712443 (MainThread): On master: COMMIT
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.714967 (MainThread): SQL status: COMMIT in 0.00 seconds
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.715144 (MainThread): On master: Close
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.715973 (MainThread): 13:15:43 | 
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.716197 (MainThread): 13:15:43 | Finished running 4 view models, 29 incremental models in 37.71s.
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.716647 (MainThread): Connection 'master' was properly closed.
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.716854 (MainThread): Connection 'model.airbyte_utils.ticket_metrics_status' was properly closed.
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.717018 (MainThread): Connection 'model.airbyte_utils.ticket_metrics_requester_wait_time_in_minutes' was properly closed.
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.717155 (MainThread): Connection 'model.airbyte_utils.ticket_metrics_reply_time_in_minutes' was properly closed.
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.717264 (MainThread): Connection 'model.airbyte_utils.ticket_metrics_on_hold_time_in_minutes' was properly closed.
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.740868 (MainThread): 
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.741120 (MainThread): Completed with 1 error and 0 warnings:
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.741463 (MainThread): 
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.741733 (MainThread): Database Error in model tickets_scd (models/generated/airbyte_incremental/scd/zendesk/tickets_scd.sql)
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.741929 (MainThread):   column "_airbyte_start_at__dbt_alter" is of type timestamp with time zone but expression is of type bigint
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.742138 (MainThread):   HINT:  You will need to rewrite or cast the expression.
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.742481 (MainThread): 
2022-04-19 13:15:50 normalization > Done. PASS=25 WARN=0 ERROR=1 SKIP=7 TOTAL=33
2022-04-19 13:15:50 normalization > 2022-04-19 13:15:43.742817 (MainThread): Flushing usage events