Airflow

Airflow is the battle-tested workflow orchestrator that runs most production data pipelines on the planet. DAGs defined in Python, massive operator library, and Airflow 3.0 (April 2025) added DAG versioning, multi-language support, and event-driven scheduling. If your data team has more than five pipelines, they probably run on Airflow.

Dagster treats data assets as first-class citizens — better for ML pipelines and teams that want lineage and observability built in. Prefect runs Python as-is with decorators, no DAG restructuring needed — faster to get started. Both are more modern, but neither has Airflow's ecosystem depth.

Use Airflow if you need a proven orchestrator with the broadest community support and your workflows are primarily schedule-based ETL/ELT.

The catch: Airflow is heavyweight — the scheduler, webserver, and workers need real infrastructure. Writing DAGs requires restructuring your code into operators and XCom patterns. The learning curve is steep for simple workflows. And Airflow's "everything is a DAG" model is awkward for event-driven or asset-centric workflows where Dagster and Prefect shine.