airflow-dag-patterns-v2 - Build Production Apache Airflow DAGs with Best Practices
Build production-ready Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment strategies.
Tags
Updated: 2026-06-30Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Design DAG structures and dependencies
- Implement custom operators and sensors
- Test Airflow DAGs locally
- Configure Airflow production deployment
- Debug failed DAG runs
- Create data pipeline orchestration
- Validate DAGs in staging environment
- Document operational runbooks
Inputs
- Apache Airflow environment
- Data sources and schedules
- Implementation playbook
- metadata.json
- ORIGIN.md
Outputs
- Airflow DAG files
- Operational runbooks
- Staging validation results
- Pull request with provenance notes
Requirements
- Codex CLI or Claude Code or Cursor or Gemini CLI or OpenCode
- Apache Airflow runtime
- Staging environment for DAG validation