airflow

Apache Airflow addresses the complexities of workflow management for engineering teams, particularly in ML and AI projects. It allows teams to programmatically author, schedule, and monitor workflows, making it easier to manage data pipelines and automate processes. With a maturity level that indicates it is a production-ready solution, Airflow has gained significant traction, evidenced by its steady growth in community interest. However, it may not be the right choice for simpler projects or teams that require a lightweight solution, as its capabilities can be overkill for less complex workflows.

Apache Airflow is a strong choice for teams needing a robust open source tool for engineering teams to manage complex data workflows and orchestration. Teams should consider alternatives when their requirements are minimal or when they need a more straightforward, user-friendly interface.

Data engineers and ML/AI teams frequently use Apache Airflow to orchestrate data workflows and manage ETL processes. It is commonly integrated into systems that require reliable data integration and automation, such as machine learning pipelines and data lakes.

Latest commit: 2026-02-13. Over the past 97 days, this repository gained 1.1k stars (+2.6% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.