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Open Source

Airflow

Platform created by the community to programmatically author, schedule and monitor workflows.

Our verdict

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About Airflow

Airflow is a community platform for programmatically authoring, scheduling, and monitoring workflows. It uses directed acyclic graphs (DAGs) defined in Python to orchestrate complex data pipelines and tasks.

Best for

Teams that need a robust, code-driven scheduler for batch-oriented data pipelines

Use cases

  • Scheduling and running ETL pipelines on a recurring basis
  • Orchestrating multi-step data processing workflows with dependencies
  • Monitoring pipeline execution and alerting on failures

Pros

  • Open source with a large community and extensive integrations
  • Python-native DAGs make pipeline logic testable and version-controllable
  • Rich UI for visualizing task status, logs, and execution history

Cons

  • Steep learning curve for configuring and managing the production environment
  • Not designed for real-time streaming or low-latency task execution
  • Scaling the scheduler and workers requires careful infrastructure planning

Key facts

What we have checked

Not checked yet. Dated facts such as prices, releases and repository activity appear here once we have confirmed them from a source.

Alternatives

Alternatives to Airflow

RecordTypeHow we knowChecked
Argo WorkflowsThis is an alternative to itOpen sourceNamed in the listingNot dated
DVCThis is an alternative to itOpen sourceNamed in the listingNot dated
KubeflowThis is an alternative to itOpen sourceNamed in the listingNot dated
PrefectThis is an alternative to itOpen sourceNamed in the listingNot dated

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Sources

Where this comes from

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Changelog

What changed

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