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apache / airflow / refs/tags/1.4.1
tagdb26311f98a8e36a35c28cd92daf370cc49ca7d1
taggerMaxime Beauchemin <maximebeauchemin@gmail.com>Mon Aug 24 08:36:12 2015 -0700
objectaa0dffdb7b735c0e95fba61583e933aa68abcc37 
Forcing flask-admin to 1.2.0 because of buggy 1.3.0
commitaa0dffdb7b735c0e95fba61583e933aa68abcc37[log] [tgz]
authorMaxime Beauchemin <maximebeauchemin@gmail.com>Mon Aug 24 08:34:52 2015 -0700
committerMaxime Beauchemin <maximebeauchemin@gmail.com>Mon Aug 24 08:34:52 2015 -0700
tree88ceed79c6ecfcbbe8848e9f7015c61a46c5dc2a
parentefd9e4c4f5dc413308fa958a8038240a38840f67 [diff]
Bugfix on Airflow 1.4.0 using flask-admin 1.3.0, pinning flask-admin to 1.2.0
2 files changed
tree: 88ceed79c6ecfcbbe8848e9f7015c61a46c5dc2a
  1. airflow/
  2. dags/
  3. docs/
  4. tests/
  5. .gitignore
  6. CONTRIBUTING.md
  7. COPYRIGHT.txt
  8. init.sh
  9. LICENSE.txt
  10. MANIFEST.in
  11. migrations.sql
  12. README.md
  13. requirements.txt
  14. run_unit_tests.sh
  15. setup.cfg
  16. setup.py
  17. TODO.md
README.md

Airflow

Airflow is a platform to programmatically author, schedule and monitor data pipelines.

![img] (http://i.imgur.com/6Gs4hxT.gif)

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.

Principles

  • Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically.
  • Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.
  • Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built into the core of Airflow using the powerful Jinja templating engine.
  • Scalable: Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.

Who uses Airflow?

As the Airflow community grows, we'd like to keep track of who is using the platform. Please send a PR with your company name and @githubhandle if you may.

Documentation

Full documentation on pythonhosted.com