🚧 D3B-2193 set up GHA to export repo to airflow

.github/workflows/export-to-airflow.yml

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+name: Export Repo to Airflow
+
+on:
+  push:
+    branches: [ "main" ]
+
+permissions:
+    contents: read
+
+jobs:
+  send-to-airflow-s3-bucket:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: send repository dispatch
+        run: |
+          curl -L \
+          -X POST \
+          -H "Accept: application/vnd.github+json" \
+          -H "Authorization: Bearer ${{ secrets.INCLUDE_AF_PAT }}" \
+          -H "X-GitHub-Api-Version: 2022-11-28" \
+          https://api.github.com/repos/include-dcc/include-dbt-airflow-mirror/dispatches \
+          -d '{"event_type":"export-to-airflow","client_payload":{"repo": "include-dbt-sandbox", "ref": "'${{ github.ref }}'", "run_id": "'${{ github.run_id }}'"}}'

dags/tutorial.py

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+"""
+This DAG is a tutorial example DAG that demonstrates the basic structure
+of an Airflow DAG and how to use the BashOperator. It's taken from the
+Airflow documentation here https://airflow.apache.org/docs/apache-airflow/stable/tutorial/fundamentals.html.
+
+This dag prints the current date, then sleeps for 5 seconds, and finally
+runs a templated bash command that prints the execution date 5 times.
+"""
+
+import textwrap
+from datetime import datetime, timedelta
+
+# The DAG object; we'll need this to instantiate a DAG
+from airflow.models.dag import DAG
+
+# Operators; we need this to operate!
+from airflow.operators.bash import BashOperator
+
+with DAG(
+    "tutorial",
+    # These args will get passed on to each operator
+    # You can override them on a per-task basis during operator initialization
+    default_args={
+        "depends_on_past": False,
+        "email": ["[email protected]"],
+        "email_on_failure": False,
+        "email_on_retry": False,
+        "retries": 1,
+        "retry_delay": timedelta(minutes=5),
+        # 'queue': 'bash_queue',
+        # 'pool': 'backfill',
+        # 'priority_weight': 10,
+        # 'end_date': datetime(2016, 1, 1),
+        # 'wait_for_downstream': False,
+        # 'sla': timedelta(hours=2),
+        # 'execution_timeout': timedelta(seconds=300),
+        # 'on_failure_callback': some_function, # or list of functions
+        # 'on_success_callback': some_other_function, # or list of functions
+        # 'on_retry_callback': another_function, # or list of functions
+        # 'sla_miss_callback': yet_another_function, # or list of functions
+        # 'on_skipped_callback': another_function, #or list of functions
+        # 'trigger_rule': 'all_success'
+    },
+    description="A simple tutorial DAG",
+    schedule=timedelta(days=1),
+    start_date=datetime(2021, 1, 1),
+    catchup=False,
+    tags=["example"],
+) as dag:
+
+    # t1, t2 and t3 are examples of tasks created by instantiating operators
+    t1 = BashOperator(
+        task_id="print_date",
+        bash_command="date",
+    )
+
+    t2 = BashOperator(
+        task_id="sleep",
+        depends_on_past=False,
+        bash_command="sleep 5",
+        retries=3,
+    )
+    t1.doc_md = textwrap.dedent(
+        """\
+    #### Task Documentation
+    You can document your task using the attributes `doc_md` (markdown),
+    `doc` (plain text), `doc_rst`, `doc_json`, `doc_yaml` which gets
+    rendered in the UI's Task Instance Details page.
+    ![img](https://imgs.xkcd.com/comics/fixing_problems.png)
+    **Image Credit:** Randall Munroe, [XKCD](https://xkcd.com/license.html)
+    """
+    )
+
+    dag.doc_md = __doc__  # providing that you have a docstring at the beginning of the DAG; OR
+    dag.doc_md = """
+    This is a documentation placed anywhere
+    """  # otherwise, type it like this
+    templated_command = textwrap.dedent(
+        """
+    {% for i in range(5) %}
+        echo "{{ ds }}"
+        echo "{{ macros.ds_add(ds, 7)}}"
+    {% endfor %}
+    """
+    )
+
+    t3 = BashOperator(
+        task_id="templated",
+        depends_on_past=False,
+        bash_command=templated_command,
+    )
+
+    t1 >> [t2, t3]