Apache Airflow
Apache Airflow is an open-source platform for programmatically authoring, scheduling, and monitoring data pipelines and batch workflows.
Quick verdict
Apache Airflow is for teams that need Python-based workflow orchestration around scraping, data, or ML pipelines. It is not a scraper or extraction API by itself.
- Best for
- Python workflow orchestration
- Scheduled scraping pipelines
- Retries and dependency management
- Open-source data teams
- Not ideal for
- Point-and-click scraping
- Standalone browser automation
- Simple one-off scripts
- Last verified
- Jun 26, 2026
Key facts
- LicenseOpen source (Apache-2.0)GitHub
- Stars43,990 GitHub starsGitHub
- Forks16,339 forksGitHub
- Downloads15,024,609 downloads/month on PyPIPyPI
- LanguagePythonGitHub
Compiled from public repository and package data; check the Apache Airflow site for current details.
Sources
Verified against the apache/airflow repository and the PyPI package.
Apache Airflow is a scalable, extensible workflow orchestration platform that empowers teams to programmatically author, schedule, and monitor complex data pipelines. Created by the open-source community, Airflow is battle-tested at organizations of every size.
Key Features:
- Pure Python - Define workflows as code using standard Python, no XML or CLI hacks
- Robust Web UI - Monitor, schedule, and manage workflows with a modern dashboard
- Extensible Operators - Plug-and-play integrations with AWS, GCP, Azure, and hundreds more
- Dynamic Pipelines - Generate pipeline DAGs dynamically with Python loops and parameters
- Scalable Architecture - Modular message queue design scales to orchestrate unlimited workers
Whether you're a data engineer, ML practitioner, or scraping team lead, Airflow provides the foundation to automate and monitor any pipeline.
Stars
44KForks
16.3KLast commit
5 months agoLicense
Apache-2.0Language
Python
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