Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows.
When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.
Table of contents
- Project Focus
- Principles
- Requirements
- Getting started
- Installing from PyPI
- Official source code
- Convenience packages
- User Interface
- Semantic versioning
- Version Life Cycle
- Support for Python and Kubernetes versions
- Base OS support for reference Airflow images
- Approach to dependencies of Airflow
- Support for providers
- Contributing
- Who uses Apache Airflow?