Programmatically author, schedule and monitor data pipelines
Project description
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# Apache Airflow
[](https://badge.fury.io/py/apache-airflow)
[](https://travis-ci.org/apache/airflow)
[](https://codecov.io/github/apache/airflow?branch=master)
[](https://airflow.readthedocs.io/en/latest/?badge=latest)
[](http://www.apache.org/licenses/LICENSE-2.0.txt)
[](https://pypi-hypernode.com/project/apache-airflow/)
[](https://twitter.com/ApacheAirflow)
_NOTE: The transition from 1.8.0 (or before) to 1.8.1 (or after) requires uninstalling Apache Airflow before installing the new version. The package name was changed from `airflow` to `apache-airflow` as of version 1.8.1._
Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows.
When workflows are defined as code, they become more maintainable,
versionable, testable, and collaborative.
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.
## Getting started
Please visit the Airflow Platform documentation (latest **stable** release) for help with [installing Airflow](https://airflow.apache.org/installation.html), getting a [quick start](https://airflow.apache.org/start.html), or a more complete [tutorial](https://airflow.apache.org/tutorial.html).
Documentation of GitHub master (latest development branch): [ReadTheDocs Documentation](https://airflow.readthedocs.io/en/latest/)
For further information, please visit the [Airflow Wiki](https://cwiki.apache.org/confluence/display/AIRFLOW/Airflow+Home).
## Beyond the Horizon
Airflow **is not** a data streaming solution. Tasks do not move data from
one to the other (though tasks can exchange metadata!). Airflow is not
in the [Spark Streaming](http://spark.apache.org/streaming/)
or [Storm](https://storm.apache.org/) space, it is more comparable to
[Oozie](http://oozie.apache.org/) or
[Azkaban](https://azkaban.github.io/).
Workflows are expected to be mostly static or slowly changing. You can think
of the structure of the tasks in your workflow as slightly more dynamic
than a database structure would be. Airflow workflows are expected to look
similar from a run to the next, this allows for clarity around
unit of work and continuity.
## 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.
## User Interface
- **DAGs**: Overview of all DAGs in your environment.
- **Tree View**: Tree representation of a DAG that spans across time.
- **Graph View**: Visualization of a DAG's dependencies and their current status for a specific run.
- **Task Duration**: Total time spent on different tasks over time.
- **Gantt View**: Duration and overlap of a DAG.
- **Code View**: Quick way to view source code of a DAG.
## Contributing
Want to help build Apache Airflow? Check out our [contributing documentation](https://github.com/apache/airflow/blob/master/CONTRIBUTING.md).
## Who uses Airflow?
As the Apache 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.
Committers:
* Refer to [Committers](https://cwiki.apache.org/confluence/display/AIRFLOW/Committers)
Currently **officially** using Airflow:
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1. [Yieldr](https://www.yieldr.com/) [[@ggeorgiadis](https://github.com/ggeorgiadis)]
1. [Zapier](https://www.zapier.com) [[@drknexus](https://github.com/drknexus) & [@statwonk](https://github.com/statwonk)]
1. [Zego](https://www.zego.com/) [[@ruimffl](https://github.com/ruimffl)]
1. [Zendesk](https://www.github.com/zendesk)
1. [Zenly](https://zen.ly) [[@cerisier](https://github.com/cerisier) & [@jbdalido](https://github.com/jbdalido)]
1. [Zymergen](https://www.zymergen.com/)
1. [99](https://99taxis.com) [[@fbenevides](https://github.com/fbenevides), [@gustavoamigo](https://github.com/gustavoamigo) & [@mmmaia](https://github.com/mmmaia)]
## Who Maintains Apache Airflow?
Airflow is the work of the [community](https://github.com/apache/airflow/graphs/contributors),
but the [core committers/maintainers](https://people.apache.org/committers-by-project.html#airflow)
are responsible for reviewing and merging PRs as well as steering conversation around new feature requests.
If you would like to become a maintainer, please review the Apache Airflow
[committer requirements](https://cwiki.apache.org/confluence/display/AIRFLOW/Committers).
## Links
- [Documentation](https://airflow.apache.org/)
- [Chat](https://apache-airflow-slack.herokuapp.com/)
- [More](https://cwiki.apache.org/confluence/display/AIRFLOW/Airflow+Links)
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
-->
# Apache Airflow
[](https://badge.fury.io/py/apache-airflow)
[](https://travis-ci.org/apache/airflow)
[](https://codecov.io/github/apache/airflow?branch=master)
[](https://airflow.readthedocs.io/en/latest/?badge=latest)
[](http://www.apache.org/licenses/LICENSE-2.0.txt)
[](https://pypi-hypernode.com/project/apache-airflow/)
[](https://twitter.com/ApacheAirflow)
_NOTE: The transition from 1.8.0 (or before) to 1.8.1 (or after) requires uninstalling Apache Airflow before installing the new version. The package name was changed from `airflow` to `apache-airflow` as of version 1.8.1._
Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows.
When workflows are defined as code, they become more maintainable,
versionable, testable, and collaborative.
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.
## Getting started
Please visit the Airflow Platform documentation (latest **stable** release) for help with [installing Airflow](https://airflow.apache.org/installation.html), getting a [quick start](https://airflow.apache.org/start.html), or a more complete [tutorial](https://airflow.apache.org/tutorial.html).
Documentation of GitHub master (latest development branch): [ReadTheDocs Documentation](https://airflow.readthedocs.io/en/latest/)
For further information, please visit the [Airflow Wiki](https://cwiki.apache.org/confluence/display/AIRFLOW/Airflow+Home).
## Beyond the Horizon
Airflow **is not** a data streaming solution. Tasks do not move data from
one to the other (though tasks can exchange metadata!). Airflow is not
in the [Spark Streaming](http://spark.apache.org/streaming/)
or [Storm](https://storm.apache.org/) space, it is more comparable to
[Oozie](http://oozie.apache.org/) or
[Azkaban](https://azkaban.github.io/).
Workflows are expected to be mostly static or slowly changing. You can think
of the structure of the tasks in your workflow as slightly more dynamic
than a database structure would be. Airflow workflows are expected to look
similar from a run to the next, this allows for clarity around
unit of work and continuity.
## 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.
## User Interface
- **DAGs**: Overview of all DAGs in your environment.
- **Tree View**: Tree representation of a DAG that spans across time.
- **Graph View**: Visualization of a DAG's dependencies and their current status for a specific run.
- **Task Duration**: Total time spent on different tasks over time.
- **Gantt View**: Duration and overlap of a DAG.
- **Code View**: Quick way to view source code of a DAG.
## Contributing
Want to help build Apache Airflow? Check out our [contributing documentation](https://github.com/apache/airflow/blob/master/CONTRIBUTING.md).
## Who uses Airflow?
As the Apache 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.
Committers:
* Refer to [Committers](https://cwiki.apache.org/confluence/display/AIRFLOW/Committers)
Currently **officially** using Airflow:
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1. [Agari](https://github.com/agaridata) [[@r39132](https://github.com/r39132)]
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1. [ARGO Labs](http://www.argolabs.org) [[California Data Collaborative](https://github.com/California-Data-Collaborative)]
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## Who Maintains Apache Airflow?
Airflow is the work of the [community](https://github.com/apache/airflow/graphs/contributors),
but the [core committers/maintainers](https://people.apache.org/committers-by-project.html#airflow)
are responsible for reviewing and merging PRs as well as steering conversation around new feature requests.
If you would like to become a maintainer, please review the Apache Airflow
[committer requirements](https://cwiki.apache.org/confluence/display/AIRFLOW/Committers).
## Links
- [Documentation](https://airflow.apache.org/)
- [Chat](https://apache-airflow-slack.herokuapp.com/)
- [More](https://cwiki.apache.org/confluence/display/AIRFLOW/Airflow+Links)
Release history Release notifications | RSS feed
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