![]() ![]() Mocking External ServicesĪ large part of our use of Airflow deals with orchestrating AWS Glue crawler and job runs in addition to interacting with other AWS services, such as S3. It gets even trickier when we start wanting to write integration tests to evaluate Airflow DAG imports and runs. ![]() Testing starts to get tricky when we want to unit test functions that have some reliance on external services such as AWS. As Airflow is Python based, we are able to apply all standard unit testing practices on many functions that we have implemented within Airflow. BackgroundĪirflow is a workflow orchestration tool that we use for scheduling our ELT processes across AWS Glue, dbt and others. These testing efforts also extend to Airflow, maintained by the data team. Leverage debugging tools to step through your DAGs and troubleshoot any errors that occur.Testing plays an important role in ensuring our code is error and bug free so that we can freely implement new features without worrying about any impact on existing functionality.Implement unit tests to verify the functionality of your DAGs.Include comments in your DAGs to document the purpose of each task and the expected output.Use a consistent naming convention for your DAGs and tasks to facilitate error tracking. ![]() Here are some additional tips to enhance your DAG testing process:
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