Workflow Automation

Apache Airflow

Workflow automation for Python users building and running data, ML and infrastructure pipelines.

Overview

Apache Airflow lets you author, schedule and monitor workflows in code. Pipelines can transfer data, build ML models or manage infrastructure, while the web app shows task status and logs. Apache Airflow is built for people with Python knowledge. You can self-host it on cloud or on-premises Kubernetes using its official Helm Chart, without tying the setup to one cloud provider.

Updated Nov 6, 2026

5 things

What Apache Airflow has

For developers

Run the software on your own servers instead of the vendor's cloud.
Libraries that help developers use a product’s API from their code.
A command-line interface that lets you run the tool from a terminal or scripts.
A toolkit that lets developers build on the product using their preferred programming language.

Extras

An online space where users can connect, share tips, and help each other.

12 features

Key Features

Scalable architecture
Apache Airflow® has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow™ is ready to scale to infinity.
Dynamic pipelines
Apache Airflow® pipelines are defined in Python, allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically.
Custom operators
Easily define your own operators and extend libraries to fit the level of abstraction that suits your environment.
Parametrization
Apache Airflow® pipelines are lean and explicit. Parametrization is built into its core using the powerful Jinja templating engine.
Python workflows
Use standard Python features to create your workflows, including date time formats for scheduling and loops to dynamically generate tasks.
Workflow management
Platform created by the community to programmatically author, schedule and monitor workflows.
Cloud operators
Apache Airflow® provides many plug-and-play operators that are ready to execute your tasks on Google Cloud Platform, Amazon Web Services, Microsoft Azure and many other third-party services.
Pipeline use cases
Anyone with Python knowledge can deploy a workflow. Apache Airflow® does not limit the scope of your pipelines; you can use it to build ML models, transfer data, manage your infrastructure, and more.
Jinja templating
Apache Airflow® pipelines are lean and explicit. Parametrization is built into its core using the powerful Jinja templating engine.
Workflow web app
Monitor, schedule and manage your workflows via a robust and modern web application. No need to learn old, cron-like interfaces. You always have full insight into the status and logs of completed and ongoing tasks.
Service operators
Apache Airflow® provides many plug-and-play operators that are ready to execute your tasks on Google Cloud Platform, Amazon Web Services, Microsoft Azure and many other third-party services.
Flexible pipelines
Anyone with Python knowledge can deploy a workflow. Apache Airflow® does not limit the scope of your pipelines; you can use it to build ML models, transfer data, manage your infrastructure, and more.

Pros and cons

Pros

Python users can define workflows with standard language features and generate pipelines dynamically.
Its modular architecture uses a message queue to coordinate workers as workloads grow.
Provider packages connect workflows to cloud services, data systems and other third-party projects.

You?

Be the first to rate Apache Airflow

Your review helps others make better decisions. No account needed. Takes about 10 seconds.

5 questions

FAQs

What does Apache Airflow do?

Apache Airflow lets you programmatically author, schedule and monitor workflows. Its web app also shows the status and logs of completed and ongoing tasks.

Who is Apache Airflow for?

Apache Airflow is for people with Python knowledge who want to deploy workflows for data transfer, ML models, infrastructure management and other pipeline tasks.

Can Apache Airflow be self-hosted?

Yes. Its official Helm Chart supports self-hosting on cloud or on-premises Kubernetes and is not tied to a specific cloud provider.

Does Apache Airflow have an API?

Yes. Apache Airflow releases an official Python API client for interacting with its application programming interface from Python code.

Which integrations does Apache Airflow support?

Active providers include Amazon, Anthropic, Apache Cassandra, Apache Hive, Apache Impala and Apache Kafka. Plug-and-play operators can run tasks on Amazon Web Services, Google Cloud Platform and Microsoft Azure.

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Community reviews

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Tried this tool? Share your experience.

Write a review

5 things

What Apache Airflow has

For developers

Run the software on your own servers instead of the vendor's cloud.
Libraries that help developers use a product’s API from their code.
A command-line interface that lets you run the tool from a terminal or scripts.
A toolkit that lets developers build on the product using their preferred programming language.

Extras

An online space where users can connect, share tips, and help each other.

12 features

Key Features

Scalable architecture
Apache Airflow® has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow™ is ready to scale to infinity.
Dynamic pipelines
Apache Airflow® pipelines are defined in Python, allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically.
Custom operators
Easily define your own operators and extend libraries to fit the level of abstraction that suits your environment.
Parametrization
Apache Airflow® pipelines are lean and explicit. Parametrization is built into its core using the powerful Jinja templating engine.
Python workflows
Use standard Python features to create your workflows, including date time formats for scheduling and loops to dynamically generate tasks.
Workflow management
Platform created by the community to programmatically author, schedule and monitor workflows.
Cloud operators
Apache Airflow® provides many plug-and-play operators that are ready to execute your tasks on Google Cloud Platform, Amazon Web Services, Microsoft Azure and many other third-party services.
Pipeline use cases
Anyone with Python knowledge can deploy a workflow. Apache Airflow® does not limit the scope of your pipelines; you can use it to build ML models, transfer data, manage your infrastructure, and more.
Jinja templating
Apache Airflow® pipelines are lean and explicit. Parametrization is built into its core using the powerful Jinja templating engine.
Workflow web app
Monitor, schedule and manage your workflows via a robust and modern web application. No need to learn old, cron-like interfaces. You always have full insight into the status and logs of completed and ongoing tasks.
Service operators
Apache Airflow® provides many plug-and-play operators that are ready to execute your tasks on Google Cloud Platform, Amazon Web Services, Microsoft Azure and many other third-party services.
Flexible pipelines
Anyone with Python knowledge can deploy a workflow. Apache Airflow® does not limit the scope of your pipelines; you can use it to build ML models, transfer data, manage your infrastructure, and more.

Pros and cons

Pros

Python users can define workflows with standard language features and generate pipelines dynamically.
Its modular architecture uses a message queue to coordinate workers as workloads grow.
Provider packages connect workflows to cloud services, data systems and other third-party projects.

You?

Be the first to rate Apache Airflow

Your review helps others make better decisions. No account needed. Takes about 10 seconds.

5 questions

FAQs

What does Apache Airflow do?

Apache Airflow lets you programmatically author, schedule and monitor workflows. Its web app also shows the status and logs of completed and ongoing tasks.

Who is Apache Airflow for?

Apache Airflow is for people with Python knowledge who want to deploy workflows for data transfer, ML models, infrastructure management and other pipeline tasks.

Can Apache Airflow be self-hosted?

Yes. Its official Helm Chart supports self-hosting on cloud or on-premises Kubernetes and is not tied to a specific cloud provider.

Does Apache Airflow have an API?

Yes. Apache Airflow releases an official Python API client for interacting with its application programming interface from Python code.

Apache Airflow

Overview

What Apache Airflow has

For developers

Extras

Key Features

Scalable architecture

Dynamic pipelines

Custom operators

Parametrization

Python workflows

Workflow management

Cloud operators

Pipeline use cases

Jinja templating

Workflow web app

Service operators

Flexible pipelines

Pros and cons

Pros

Be the first to rate Apache Airflow

FAQs

What does Apache Airflow do?

Who is Apache Airflow for?

Can Apache Airflow be self-hosted?

Does Apache Airflow have an API?

Which integrations does Apache Airflow support?

What Apache Airflow has

For developers

Extras

Key Features

Scalable architecture

Dynamic pipelines

Custom operators

Parametrization

Python workflows

Workflow management

Cloud operators

Pipeline use cases

Jinja templating

Workflow web app

Service operators

Flexible pipelines

Pros and cons

Pros

Be the first to rate Apache Airflow

FAQs

What does Apache Airflow do?

Who is Apache Airflow for?

Can Apache Airflow be self-hosted?

Does Apache Airflow have an API?

Which integrations does Apache Airflow support?

More in Workflow Automation

More in Workflow Automation