AirFlow
ORCHESTRATION
Apache Airflow is an open-source platform designed to programmatically author, schedule, and monitor workflows. It allows users to define tasks and their dependencies as code, enabling dynamic pipeline generation and complex data orchestration. Airflow supports a rich set of integrations and extensibility through custom plugins and operators.
Apache Airflow + MotherDuck
Apache Airflow integrates with MotherDuck by leveraging custom operators to connect to the MotherDuck cloud data warehouse. This allows users to execute SQL queries and manage data workflows directly within the Airflow environment, utilizing DuckDB's efficient analytical capabilities in a cloud-native setting.
FAQS
Can I use Apache Airflow with MotherDuck?
Yes, you can orchestrate MotherDuck workflows with Apache Airflow. Use the DuckDB Python library within your Airflow tasks to connect to MotherDuck using the connection string md:your_database?motherduck_token=YOUR_TOKEN. This lets you schedule and orchestrate data pipelines that load, transform, or export data from MotherDuck.
What's the best way to connect Airflow to MotherDuck?
Use the DuckDB Python library (pip install duckdb) in your Airflow PythonOperator or custom operators. Store your MotherDuck token as an Airflow Variable or Connection secret, then connect with duckdb.connect('md:database?motherduck_token=...'). For managed Airflow, consider Astronomer which has DuckDB/MotherDuck experience.
Can I use Airflow to schedule dbt runs with MotherDuck?
Yes, combine Airflow with dbt-duckdb to schedule transformation workflows. Use the BashOperator or dbt Cloud operator to trigger dbt runs that execute against MotherDuck. This gives you the orchestration power of Airflow with dbt's transformation capabilities on MotherDuck's serverless analytics.
