Why?

You might ask - why a separate package and not just shove everything into the dags folder of Airflow.

That works for development. However, we are running more and more business-critical work-flows on Airflow. In order to ensure business continuity and reduce the risk of errors, we need more reliable and maintainable building blocks.

• Maintainability: You can reuse the components across multiple DAGs.
• Quality: This repository aims to set a new standard in the internal Python eco-system, hence we ensure
  • ✨ High code-quality with aggressive linters and strict PR policies
  • ✒️ Well-documented public API and examples
  • 📦 Transparent release cycle
  • 🧪 High test coverage
• Reusability: You can reuse the components across multiple DAGs.

Versioning

The package follows semantic versioning. Breaking changes will occur unannounced before v1.0.0. After that all breaking changes will lead to bumping the major version number.

Contact

The project is hosted at github.com/dfds-data/dagcellent.

If you have a feature request, noticed a bug - it is best to open a new issue on that page.