Apache Airflow
Workflow orchestration platform
The score summarizes repository activity. Funding is unknown and excluded from the score. These repository signals do not establish financial stability, security, or maintainer availability. How to interpret this data.
Evidence and limitations
Activity source: apache/airflow via the GitHub API. Counts describe this repository, which may be a mirror or only part of the project.
- Commits in 30 days
- 988
- Commits in 90 days
- 2468
- Authors accounting for half the sampled commits
- 9
The 200-commit author sample does not cover the full 90 days. Author counts are lower bounds; concentration describes the sample only.
Few commits can reflect mature software. Commit authors do not establish merge rights, available maintainers, organizational diversity, or knowledge distribution. Funding is unknown; donation links alone do not establish income or runway.
Correct this assessment or provide evidence →Ways to help Apache Airflow
We have not yet checked this project's specific support needs.
Read the repository's contribution and security guidance before proposing work.
Overview
Apache Airflow is a platform to programmatically author, schedule, and monitor workflows using directed acyclic graphs (DAGs).
Importance
- Leading workflow orchestration tool
- Powers data pipelines worldwide
- Foundation for data engineering
- Critical for ETL processes
Key Features
- Python-based DAG authoring
- Extensive operator library
- Web-based UI
- Scheduling and monitoring
Sustainability
Financial sustainability and maintainer capacity have not yet been reviewed against current primary sources. See the evidence and support sections above for available information and a way to suggest corrections.
Dependencies
