Airflow DAG Patterns

About

Architect robust data pipelines with expert guidance on Apache Airflow DAG design, implementation, and deployment. This skill provides production-tested patterns for the TaskFlow API, dynamic DAG generation, and complex branching logic while prioritizing idempotency and observability. It includes comprehensive strategies for custom operators, resource-efficient sensors, and automated testing, ensuring your data orchestration workflows are scalable, reliable, and maintainable.

Key Features

• Standardized error handling strategies and automated alerting callbacks
• Dynamic DAG factory generation for managing multiple similar pipelines
• 23,110 GitHub stars
• Implementation of TaskFlow API and decorator-based workflow patterns
• Resource-efficient sensor patterns using reschedule mode and custom logic
• Comprehensive testing frameworks for DAG integrity and task-level validation

Use Cases

• Creating idempotent and atomic ETL data pipelines
• Architecting complex workflow dependencies and conditional branching logic
• Establishing local testing and production deployment strategies for Airflow