Jun 2015
Apache-2.0 License
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21 Oct
Github last commit
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What is Airflow?

Apache Airflow helps visualize the data pipeline's progress, dependencies, code, and success status. Airbnb launched Airflow in 2015, and it is currently supported by 1700 contributors and an ever-growing community.
Airflow allows using Directed Acyclic Graphs (DAGs) to manage workflow orchestration. Airflow facilitates visualizing pipelines running in production, tracking progress, and troubleshooting issues as and when needed. The Python-based tool allows easy integration with other data sources and generates email or slack alerts when a task completes or fails.

How Does Airflow Help?

Many machine learning tasks involve setting up data pipelines where multiple components execute at various stages, and each one depends on others in complex ways. Scheduling these components using Cron is a challenging task but Airflow simplifies it.

Airflow helps execute these tasks by:

  • Creating custom DAGs and map dependencies
  • Monitoring the status and logs of the jobs to infer about plans and troubleshoot issues
  • Handling complex and mixed-mode tasks
  • Mitigating upstream issues and managing delayed arrival of data by backfiling historical data and retrying failed jobs
  • Serving complex and custom use cases with custom hook/operators and plugins
  • Standardizing ETL workflow orchestration with powerful web UI and concurrency management

Key Features of Airflow

Scalability and modular architecture 

Airflow uses a message queue to orchestrate workers and scale infinitely. It allows defining custom operators and extending libraries to attain the required level of abstraction.

Python-based tool

Airflow uses standard Python features to develop workflows for scheduling and loops to generate tasks offering more flexibility than XML and command-line experience.

Insightful visualization

Airflow facilitates monitoring, planning, and managing workflows through a robust and modern web application. Users have full insight into the status and logs of completed and running tasks.

Robust integrations

Airflow seamlessly integrates with Google Cloud Platform, Amazon Web Services, Microsoft Azure, and several other third-party services. 

Easy to use

Airflow is easy to use and enables anyone with Python knowledge to deploy a workflow. It supports building ML models, transferring data, and managing infrastructure.

Community support

Airflow is backed by strong community support and active contributors who willingly share their experiences. 

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