airflow-dag-patterns

What problem does it solve?

This Skill provides battle-tested patterns and best practices for creating robust, maintainable, and efficient Apache Airflow Directed Acyclic Graphs (DAGs).

Core Features & Use Cases

• DAG Design: Learn principles like idempotency, atomicity, and observability.
• Task Dependencies: Understand various ways to define task relationships.
• Advanced Patterns: Implement TaskFlow API, dynamic DAG generation, branching, and sensors.
• Error Handling: Set up callbacks and use trigger rules for resilience.
• Testing: Write unit tests for your DAGs and tasks.
• Use Case: You need to build a complex data pipeline that ingests data from multiple sources, transforms it, and loads it into a data warehouse, with robust error handling and scheduling.

Quick Start

Use the airflow-dag-patterns skill to create a new DAG file named my_etl_dag.py that extracts data daily from an S3 bucket and loads it into a Redshift table.