This skill provides a comprehensive library of architectural patterns for Apache Airflow, enabling developers to build robust, idempotent, and observable data pipelines. It covers essential Airflow 2.0+ features like the TaskFlow API, dynamic DAG generation, and sophisticated branching logic, alongside production necessities like custom sensors, failure callbacks, and automated testing strategies. Whether you are orchestrating complex ETL processes or scheduling simple batch jobs, this skill ensures your workflows follow industry best practices for reliability and maintainability.
Key Features
01Dynamic DAG factory patterns for scalable pipeline generation
02TaskFlow API implementation for modern Pythonic DAG definitions
03Comprehensive error handling and custom alerting callbacks
042 GitHub stars
05Standardized unit testing patterns for DAG integrity validation
06Advanced sensor integration including S3 and external task dependencies
