Apache Airflow

What it is

Apache Airflow is an open-source platform designed to programmatically author, schedule, and monitor workflows. It is written in Python and allows users to define workflows as Directed Acyclic Graphs (DAGs) of tasks. Airflow's architecture is highly extensible, supporting a wide range of integrations with various data sources and services. It is widely used in data engineering and data science for orchestrating complex data pipelines, ETL processes, and machine learning workflows. Airflow's scheduler executes tasks on an array of workers while following the specified dependencies, and its web-based UI provides visibility into the state of workflows and tasks.

Extensible DAG-based architecture for complex workflow orchestration.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Complex setup and configuration

   Requires knowledge of Python and DAGs for initial setup.

2. 02

   High resource consumption

   Airflow can be resource-intensive, especially for large-scale workflows.

What it does well

• Highly extensible with plugins

  Supports custom operators and hooks for integration with external systems.

• Active community and development

  Over 4,575 contributors on GitHub.

• Web-based UI for monitoring

  Provides a user-friendly interface to track task progress and status.

• Open-source and free to use

  Licensed under Apache-2.0.

Pricing

Free tier

Yes

Paid from

—

Model

N/A

Enterprise plan

No

Pricing last confirmed Sep 29, 2026.