Databricks recommends using DatabricksRunNowOperator because it reduces duplication of job definitions and job runs triggered with this operator are easy to find in the jobs UI. The DatabricksRunNowOperator requires an existing Azure Databricks job and uses the Trigger a new job run ( POST /jobs/run-now) API request to trigger a run.The Airflow Azure Databricks integration provides two different operators for triggering jobs: Click the DAG name to view details, including the run status of the DAG.Trigger the example DAG by clicking the Start button.Click the Pause/Unpause DAG toggle to unpause one of the example DAGs, for example, the example_python_operator.In a browser window, open The Airflow DAGs screen appears.To verify the Airflow installation, you can run one of the example DAGs included with Airflow: To run it, open a new terminal and run the following command: pipenv shell The scheduler is the Airflow component that schedules DAGs. To start the web server, open a terminal and run the following command: airflow webserver The Airflow web server is required to view the Airflow UI. Start the Airflow web server and scheduler To install extras, for example celery and password, run: pip install "apache-airflow" The SQLite database and default configuration for your Airflow deployment are initialized in the airflow directory. In a production Airflow deployment, you would configure Airflow with a standard database. Initialize a SQLite database that Airflow uses to track metadata.Airflow uses the dags directory to store DAG definitions. Install Airflow and the Airflow Databricks provider packages.Initialize an environment variable named AIRFLOW_HOME set to the path of the airflow directory.This isolation helps reduce unexpected package version mismatches and code dependency collisions. Databricks recommends using a Python virtual environment to isolate package versions and code dependencies to that environment. Use pipenv to create and spawn a Python virtual environment.Create a directory named airflow and change into that directory.Pipenv install apache-airflow-providers-databricksĪirflow users create -username admin -firstname -lastname -role Admin -email you copy and run the script above, you perform these steps: Be sure to substitute your user name and email in the last line: mkdir airflow To install the Airflow Azure Databricks integration, open a terminal and run the following commands. ![]() Install the Airflow Azure Databricks integration The examples in this article are tested with Python 3.8.
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