airflow

Apache Airflow addresses the need for orchestrating complex workflows in data engineering and automation. It allows backend/API teams, DevOps/platform teams, and engineering managers to programmatically author, schedule, and monitor workflows, making it a production-ready solution suitable for diverse applications. With a steady increase in community interest, indicated by 958 stars gained over 85 days, Airflow demonstrates its maturity and reliability. However, it may not be the right choice for teams requiring simple task scheduling or those with minimal workflow complexity, as its capabilities can introduce unnecessary overhead in such cases.

Apache Airflow is a strong choice for teams managing intricate data pipelines and workflows that require robust scheduling and monitoring capabilities. Teams should consider alternatives when their needs are simpler or when they seek a more lightweight solution.

Airflow is particularly beneficial for backend/API teams, DevOps/platform teams, and ML/AI teams who need to manage data workflows effectively. It is commonly integrated into data processing systems, ETL pipelines, and machine learning model training workflows.

Latest commit: 2026-02-03. Over the past 86 days, this repository gained 958 stars (+2.2% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.