Science museum workflow

src/cc_catalog_airflow/dags/science_museum_workflow.py

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+"""
+This file configures the Apache Airflow DAG to ingest Science museum data.
+
+We do this by running `provider_api_scripts.science_museum.main`
+"""
+# airflow DAG (necessary for Airflow to find this file)
+from datetime import datetime, timedelta
+import logging
+
+from provider_api_scripts import science_museum
+from util.dag_factory import create_provider_api_workflow
+
+logging.basicConfig(
+    format='%(asctime)s - %(name)s - %(levelname)s:  %(message)s',
+    level=logging.INFO
+)
+
+logger = logging.getLogger(__name__)
+
+DAG_ID = 'science_museum_workflow'
+START_DATE = datetime(2020, 1, 1)
+DAGRUN_TIMEOUT = timedelta(hours=24)
+
+globals()[DAG_ID] = create_provider_api_workflow(
+    DAG_ID,
+    science_museum.main,
+    start_date=START_DATE,
+    schedule_string='@monthly',
+    dated=False,
+    dagrun_timeout=DAGRUN_TIMEOUT
+)

src/cc_catalog_airflow/dags/test_science_museum_workflow.py

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+import os
+from airflow.models import DagBag
+
+FILE_DIR = os.path.abspath(os.path.dirname(__file__))
+
+
+def test_dag_loads_with_no_errors(tmpdir):
+    tmp_directory = str(tmpdir)
+    print(tmp_directory)
+    dag_bag = DagBag(dag_folder=tmp_directory, include_examples=False)
+    dag_bag.process_file(
+        os.path.join(FILE_DIR, 'science_museum_workflow.py')
+    )
+    print(dag_bag.dags)
+    assert len(dag_bag.import_errors) == 0
+    assert len(dag_bag.dags) == 1