A test environment for Apache Airflow that lets you carry out local development and testing of Apache Airflow workflows has been made available as open source. Whirl uses Docker and Docker compose to start up Apache Airflow and the other components used in your workflow.

Whirl was developed at GoDataDriven as part of its Open Source Initiative.

Apache Airflow is a workflow automation and scheduling system that you can use to set up and manage data pipelines. It uses workflows made of directed acyclic graphs (DAGs) of tasks. Each task produces some output that is then used as the input to another task.

The idea of Whirl is to make it easy to run and develop Airflow workflows on your local machine. This gives you rapid feedback about whether the changes you made to your DAG work. The developers suggest you think of it as your integration test environment for developing Airflow DAGs.

The way Whirl works is that it uses Docker containers to start up Apache Airflow and the other components that your workflow relies on. This gives you a copy of your production environment that runs on your local machine.

This allows you to run your DAG locally all the way through using the actual code you would use on the production environment. You can try things out isolated from the actual workflows and see how it works with test data.  Whirl connects the code of your DAG and your test data to the Apache Airflow container that it spins up. By using volume mounts, you can make changes to your code in your IDE and immediately see the effect in the running Apache Airflow UI on your machine. This even works with custom Python modules that you are developing and using in your DAGs.

More Information

Whirl On GitHub

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