Update example DAG to DevRel-owned consistent example (#19)

* Update example DAG to DevRel-owned consistent example

* Apply suggestions from code review

Co-authored-by: Tamara Janina Fingerlin <90063506+TJaniF@users.noreply.github.com>

* Apply suggestions from code review

Co-authored-by: Tamara Janina Fingerlin <90063506+TJaniF@users.noreply.github.com>

* Apply suggestions from code review

---------

Co-authored-by: Tamara Janina Fingerlin <90063506+TJaniF@users.noreply.github.com>

Author: kentdanas

Dockerfile

@@ -1,2 +1 @@
-FROM quay.io/astronomer/astro-runtime:10.4.0
-ADD scripts/s3_countries_transform_script.sh /usr/local/airflow/transform_script.sh
+FROM quay.io/astronomer/astro-runtime:10.4.0

README.md

@@ -8,9 +8,8 @@ Project Contents
 
 Your Astro project contains the following files and folders:
 
-- dags: This folder contains the Python files for your Airflow DAGs. By default, this directory includes two example DAGs:
-    - `example_dag_basic`: This DAG shows a simple ETL data pipeline example with three TaskFlow API tasks that run daily.
-    - `example_dag_advanced`: This advanced DAG showcases a variety of Airflow features like branching, Jinja templates, task groups and several Airflow operators.
+- dags: This folder contains the Python files for your Airflow DAGs. By default, this directory includes one example DAG:
+    - `example_astronauts`: This DAG shows a simple ETL pipeline example that queries the list of astronauts currently in space from the Open Notify API and prints a statement for each astronaut. The DAG uses the TaskFlow API to define tasks in Python, and dynamic task mapping to dynamically print a statement for each astronaut. For more on how this DAG works, see our [Getting started tutorial](https://docs.astronomer.io/learn/get-started-with-airflow). 
 - Dockerfile: This file contains a versioned Astro Runtime Docker image that provides a differentiated Airflow experience. If you want to execute other commands or overrides at runtime, specify them here.
 - include: This folder contains any additional files that you want to include as part of your project. It is empty by default.
 - packages.txt: Install OS-level packages needed for your project by adding them to this file. It is empty by default.

dags/example_astronauts.py

@@ -0,0 +1,79 @@
+"""
+## Astronaut ETL example DAG
+
+This DAG queries the list of astronauts currently in space from the 
+Open Notify API and prints each astronaut's name and flying craft.
+
+There are two tasks, one to get the data from the API and save the results,
+and another to print the results. Both tasks are written in Python using
+Airflow's TaskFlow API, which allows you to easily turn Python functions into
+Airflow tasks, and automatically infer dependencies and pass data.
+
+The second task uses dynamic task mapping to create a copy of the task for
+each Astronaut in the list retrieved from the API. This list will change
+depending on how many Astronauts are in space, and the DAG will adjust 
+accordingly each time it runs.
+
+For more explanation and getting started instructions, see our Write your 
+first DAG tutorial: https://docs.astronomer.io/learn/get-started-with-airflow
+
+![Picture of the ISS](https://www.esa.int/var/esa/storage/images/esa_multimedia/images/2010/02/space_station_over_earth/10293696-3-eng-GB/Space_Station_over_Earth_card_full.jpg)
+"""
+
+from airflow import Dataset
+from airflow.decorators import dag, task
+from pendulum import datetime
+import requests
+
+#Define the basic parameters of the DAG, like schedule and start_date
+@dag(
+    start_date=datetime(2024, 1, 1),
+    schedule="@daily",
+    catchup=False,
+    doc_md=__doc__,
+    default_args={"owner": "Astro", "retries": 3},
+    tags=["example"],
+)
+def example_astronauts():
+    #Define tasks
+    @task(
+        #Define a dataset outlet for the task. This can be used to schedule downstream DAGs when this task has run.
+        outlets=[Dataset("current_astronauts")]
+    )  # Define that this task updates the `current_astronauts` Dataset
+    def get_astronauts(**context) -> list[dict]:
+        """
+        This task uses the requests library to retrieve a list of Astronauts 
+        currently in space. The results are pushed to XCom with a specific key
+        so they can be used in a downstream pipeline. The task returns a list
+        of Astronauts to be used in the next task.
+        """
+        r = requests.get("http://api.open-notify.org/astros.json")
+        number_of_people_in_space = r.json()["number"]
+        list_of_people_in_space = r.json()["people"]
+
+        context["ti"].xcom_push(
+            key="number_of_people_in_space", value=number_of_people_in_space
+        )
+        return list_of_people_in_space
+
+    @task
+    def print_astronaut_craft(greeting: str, person_in_space: dict) -> None:
+        """
+        This task creates a print statement with the name of an 
+        Astronaut in space and the craft they are flying on from 
+        the API request results of the previous task, along with a 
+        greeting which is hard-coded in this example.
+        """
+        craft = person_in_space["craft"]
+        name = person_in_space["name"]
+
+        print(f"{name} is currently in space flying on the {craft}! {greeting}")
+
+    #Use dynamic task mapping to run the print_astronaut_craft task for each 
+    #Astronaut in space
+    print_astronaut_craft.partial(greeting="Hello! :)").expand(
+        person_in_space=get_astronauts() #Define dependencies using TaskFlow API syntax
+    )
+
+#Instantiate the DAG
+example_astronauts()