sas-airflow-provider

import pendulum from airflow.models.dag import DAG from sas_airflow_provider.operators.sas_studio import SASStudioOperator import os # NOTE: For a real deployment, configure your SAS connection in the Airflow UI. # (Admin -> Connections, Connection Id: 'sas_default', Connection Type: 'SAS') # Fill in Host, Login, Password or use 'Extra' JSON for OAuth token. # Example 'Extra' for OAuth: {"token": "your_oauth_token_here"} # Or for global variable: {"token_variable": "airflow_variable_name"} # The connection below is for demonstration if env vars are used for local testing. # Mock environment variables for connection for local testing (NOT PRODUCTION BEST PRACTICE) os.environ['AIRFLOW_CONN_SAS_DEFAULT'] = ( 'sas://' + os.environ.get('SAS_DEFAULT_LOGIN', 'user') + ':' + os.environ.get('SAS_DEFAULT_PASSWORD', 'password') + '@' + os.environ.get('SAS_DEFAULT_HOST', 'https://your-sas-viya-host.com') ) with DAG( dag_id="sas_studio_flow_example", start_date=pendulum.datetime(2023, 1, 1, tz="UTC"), catchup=False, schedule=None, tags=["sas", "studio", "example"], ) as dag: run_my_sas_flow = SASStudioOperator( task_id="run_sas_studio_flow_task", path="/Public/my_airflow_test_flow", # Replace with your actual SAS Studio Flow path connection_id="sas_default", exec_type="flow", # Can also be 'program' for SAS programs # Optional: pass macro variables to the flow # macro_variables={"input_param": "airflow_value"}, # Optional: retrieve SAS logs to Airflow # job_exec_log=True, )