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Apache Airflow

Platform to programmatically author, schedule, and monitor workflows defined as Python code.

Apache Airflow is a platform for authoring, scheduling, and monitoring batch workflows as code. Workflows are defined as DAGs in Python, which makes them versionable and testable. Data and platform teams run it self-hosted or through third-party managed services.

Documented features

• Defines workflows as DAGs in Python code, enabling dynamic generation and parameterization
• Scheduler executes tasks on an array of workers following dependencies
• Supports multiple executor types for distributed task execution
• Web UI with DAG overview, grid view over time, and task dependency visualization
• Code viewing and backfill from the UI
• Built-in operators with an extensible provider ecosystem for external services
• Official Docker images, Kubernetes deployment, and a Helm chart
• Supports PostgreSQL, MySQL, and SQLite database backends

Feature summaries come from the project's own repository and documentation and describe the software as documented on the verification date, not an evaluation.