airflow-dag-patterns

Build production-ready Airflow DAGs using the TaskFlow API by structuring data pipelines with Python decorators. This approach simplifies operator usage and dependency management for orchestrating workflows in development and production environments.

Handle external dependencies in Airflow pipelines by implementing sensors that pause task execution until specific criteria are met. This pattern ensures downstream batch jobs only run when required upstream data or files are available.

Implement branching and conditional logic in Airflow orchestration by using branching operators to route tasks dynamically. This pattern allows workflows to execute different downstream paths based on upstream task results.

You can dynamically generate Airflow DAGs to schedule multiple batch jobs programmatically. This pattern loops through configurations to instantiate distinct DAG objects, reducing code duplication when orchestrating workflows.

Add error handling and alerts to Airflow data pipelines by configuring task retry policies and failure callbacks. This pattern ensures robust orchestration by notifying teams and executing recovery logic when task failures occur.

Testing Airflow DAGs before deployment requires validating DAG loading logic and TaskFlow API behavior. Limitations include mocking external dependencies for sensors, which can complicate end-to-end pipeline testing in local development environments.