What is Airflow used for?

Airflow is an Apache workflow orchestration platform where data teams author, schedule and monitor pipelines as Python code, widely used for data engineering and ELT jobs. It is a good fit for orchestrating data pipelines and ELT jobs, scheduling machine learning workflows, and coordinating tasks across databases and cloud services.

Is Airflow open source?

Yes. Airflow is open source under the Apache-2.0 licence. Its source code is on GitHub at apache/airflow and is written mainly in Python.

Yes. Airflow is open source, so the software itself is free to use.

Can I self-host Airflow?

Yes. Airflow can be self-hosted on your own server or infrastructure.

What is Airflow an alternative to?

Airflow is an open-source alternative to Astronomer and Azure Data Factory. Other open-source alternatives to Astronomer include Prefect, Dagster and Kestra.

Is Airflow actively maintained?

Yes. The most recent commit to Airflow was on 2 October 2026, and the latest release is 3.3.2, published on 17 September 2026. The project has 47k stars on GitHub.

Airflow: open-source alternative to Astronomer

About Airflow

Apache Airflow is a workflow platform where you write, schedule and watch over pipelines in code. Workflows are defined as Python code in the form of directed acyclic graphs, or DAGs, where each task and its dependencies are explicit, so pipelines can be versioned, tested and reviewed like any other software.

A scheduler runs tasks on a defined cadence and workers execute them, while a web interface shows the status of each run, lets you inspect logs and retry failures. Airflow is used heavily in data engineering, including data integration, ELT and ETL pipelines, data orchestration and machine learning workflows, and its topics cover data science and automation. It has a large ecosystem of providers and operators for connecting to databases, cloud services and other systems, and the 3.x line is the current series.

Airflow is an Apache Software Foundation project under the Apache-2.0 license and runs on your own infrastructure, from a single machine to Kubernetes. Several vendors offer managed Airflow services, but the project itself is self-hosted software. It suits data engineers who prefer code-first orchestration over drag-and-drop tools.

Key features

Workflows defined as Python DAGs
Scheduler for recurring and dependent tasks
Web UI for monitoring runs and logs
Retries and task dependency management
Large provider ecosystem of integrations
Runs on single machines or Kubernetes

Good fit for

→Orchestrating data pipelines and ELT jobs
→Scheduling machine learning workflows
→Coordinating tasks across databases and cloud services

Built with: Python
Tags: workflow-orchestration; data-engineering; scheduler; python; dag; etl; apache; automation; data-pipelines

Airflow: questions and answers

What is Airflow used for?: Airflow is an Apache workflow orchestration platform where data teams author, schedule and monitor pipelines as Python code, widely used for data engineering and ELT jobs. It is a good fit for orchestrating data pipelines and ELT jobs, scheduling machine learning workflows, and coordinating tasks across databases and cloud services.
Is Airflow open source?: Yes. Airflow is open source under the Apache-2.0 licence. Its source code is on GitHub at apache/airflow and is written mainly in Python.
Is Airflow free?: Yes. Airflow is open source, so the software itself is free to use.
Can I self-host Airflow?: Yes. Airflow can be self-hosted on your own server or infrastructure.
What is Airflow an alternative to?: Airflow is an open-source alternative to Astronomer and Azure Data Factory. Other open-source alternatives to Astronomer include Prefect, Dagster and Kestra.
Is Airflow actively maintained?: Yes. The most recent commit to Airflow was on 2 October 2026, and the latest release is 3.3.2, published on 17 September 2026. The project has 47k stars on GitHub.