Apache Airflow DAG Patterns

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This skill empowers Claude with specialized knowledge for architecting robust data pipelines using Apache Airflow. It enables the creation of idempotent, atomic, and observable workflows while implementing advanced patterns like the TaskFlow API, dynamic DAG generation, and complex branching logic. Whether you are debugging failed runs, setting up custom sensors, or implementing error-handling callbacks, this skill ensures your orchestration logic follows production-ready standards for reliability and maintainability in complex data environments.

Características Principales

• Dynamic DAG generation for scalable pipeline factories
• Standardized testing patterns for DAG validation using pytest
• Production error handling and failure callback strategies
• Advanced sensor configurations and external task dependencies
• Implementation of Airflow 2.0+ TaskFlow API patterns
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Casos de Uso

• Implementing robust error handling and alerting for mission-critical data jobs
• Designing complex ETL/ELT data orchestration workflows with multi-task dependencies
• Automating the creation of multiple similar pipelines through dynamic factory patterns