Workflow orchestration · Apache Software Foundation

Apache Airflow

The de facto standard open-source workflow orchestrator, defining pipelines as Python DAGs of scheduled, dependent tasks.

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Apache Airflow is an open-source platform for authoring, scheduling, and monitoring workflows, defined as directed acyclic graphs (DAGs) of tasks written in Python. It became the default choice for batch data-pipeline orchestration over the last decade, with a large ecosystem of provider packages connecting to virtually every warehouse, cloud service, and API. Airflow schedules tasks on a time or event basis, retries failures, tracks task state, and surfaces a web UI for monitoring runs and logs. It has no first-party hosted service of its own; teams either self-host the scheduler, webserver, and workers (often on Kubernetes), or use a managed distribution such as Astronomer, Google Cloud Composer, or AWS MWAA. Airflow 3's DAG-versioning and event-driven scheduling narrowed some historical gaps with newer orchestrators like Dagster and Prefect, though its task-centric (rather than asset-centric) model remains its defining trait.

At a glance

Vendor Apache Software Foundation
Pricing model Open source + paid options
Free tier Yes
Deployment Self-hosted
Open source Yes (Apache-2.0)
Best for Teams needing a mature, widely supported orchestrator with the deepest ecosystem of integrations.

Pricing

Airflow itself is free, open-source software; managed hosting (Astronomer, Cloud Composer, MWAA) is priced separately by those vendors.

Pricing has not been verified yet — see the vendor's site.

Features

  • Pipelines defined as Python DAGs of tasks
  • Large ecosystem of provider packages/operators
  • Time-based and event-driven (Airflow 3) scheduling
  • Web UI for monitoring runs, logs, and task state
  • Task retries, SLAs, and alerting
  • Kubernetes and CeleryExecutor for distributed execution
  • REST API for programmatic pipeline management

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Dagster Asset-centric orchestrator that models pipelines around the data they produce, with built-in testing, typing, and observability. Prefect Python-native workflow orchestrator built around plain functions and dynamic flows, with a managed cloud control plane for scheduling and observability. Astronomer Managed Apache Airflow platform (Astro) that runs, scales, and supports Airflow for teams who don't want to operate it themselves.

Apache Airflow in the index now

Terms to know

Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. ETL (extract, transform, load) A data integration pattern that transforms data before loading it, unlike ELT which transforms after loading. ELT (extract, load, transform) A data integration pattern that loads raw data first and transforms it afterward using the warehouse's own compute. Workflow orchestration Scheduling, sequencing and monitoring interdependent data tasks so each step runs only after the ones it depends on succeed.

Related guides

How to build a modern data stack Ingestion, warehouse, transformation, BI and activation — what a data stack needs at each company stage, and what to defer. How to choose a workflow orchestrator Pick a workflow orchestrator by pipeline shape, who owns it, and whether you already run Kubernetes — not by the demo DAG. How to decide build vs buy for analytics A framework for the build-vs-buy decision in analytics infrastructure — total cost, maintenance burden and lock-in, layer by layer.