Apache Airflow
The most widely deployed open-source workflow orchestrator
Apache Airflow is profiled here as a Pipeline Orchestration tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Apache Airflow is an open-source workflow orchestrator created by Maxime Beauchemin at Airbnb in 2014 and a top-level Apache Software Foundation project since 2019. Pipelines are Python DAGs, so scheduling logic lives in version control next to the code it runs. Airflow 3, released in April 2025, added DAG versioning, event-driven scheduling, and a rebuilt React interface. Thousands of organizations run Airflow in production, and the provider ecosystem packages operators for most databases, clouds, and SaaS platforms a data team touches.
Key Capabilities:
Python-defined DAGs with dynamic task mapping
Cron, dataset-aware, and event-driven scheduling
Provider packages for major clouds, databases, and SaaS systems
Deferrable operators for resource-efficient long waits
REST API and a modern web UI for operations
Managed offerings from Astronomer, AWS MWAA, and Google Cloud Composer
Alternative tools
- Flyte
Kubernetes-native orchestration for data and ML workflows
- Kestra
Declarative, event-driven orchestration defined in YAML
- Temporal
Durable execution for long-running, reliable workflows
- Prefect
Python-native orchestration for data and ML workflows
- Dagster
Asset-centric orchestration for data and ML pipelines