{ "info": { "author": "Apache Software Foundation", "author_email": "dev@airflow.apache.org", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "Environment :: Console", "Environment :: Web Environment", "Framework :: Apache Airflow", "Intended Audience :: Developers", "Intended Audience :: System Administrators", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Topic :: System :: Monitoring" ], "description": "\n\n# Apache Airflow\n\n[![PyPI version](https://badge.fury.io/py/apache-airflow.svg)](https://badge.fury.io/py/apache-airflow)\n[![GitHub Build](https://github.com/apache/airflow/workflows/CI%20Build/badge.svg)](https://github.com/apache/airflow/actions)\n[![Coverage Status](https://img.shields.io/codecov/c/github/apache/airflow/main.svg)](https://codecov.io/github/apache/airflow?branch=main)\n[![License](https://img.shields.io/:license-Apache%202-blue.svg)](https://www.apache.org/licenses/LICENSE-2.0.txt)\n[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/apache-airflow.svg)](https://pypi.org/project/apache-airflow/)\n[![Docker Pulls](https://img.shields.io/docker/pulls/apache/airflow.svg)](https://hub.docker.com/r/apache/airflow)\n[![Docker Stars](https://img.shields.io/docker/stars/apache/airflow.svg)](https://hub.docker.com/r/apache/airflow)\n[![PyPI - Downloads](https://img.shields.io/pypi/dm/apache-airflow)](https://pypi.org/project/apache-airflow/)\n[![Artifact HUB](https://img.shields.io/endpoint?url=https://artifacthub.io/badge/repository/apache-airflow)](https://artifacthub.io/packages/search?repo=apache-airflow)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![Twitter Follow](https://img.shields.io/twitter/follow/ApacheAirflow.svg?style=social&label=Follow)](https://twitter.com/ApacheAirflow)\n[![Slack Status](https://img.shields.io/badge/slack-join_chat-white.svg?logo=slack&style=social)](https://s.apache.org/airflow-slack)\n\n[Apache Airflow](https://airflow.apache.org/docs/apache-airflow/stable/) (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows.\n\nWhen workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.\n\nUse 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.\n\n\n\n**Table of contents**\n\n- [Project Focus](#project-focus)\n- [Principles](#principles)\n- [Requirements](#requirements)\n- [Getting started](#getting-started)\n- [Installing from PyPI](#installing-from-pypi)\n- [Official source code](#official-source-code)\n- [Convenience packages](#convenience-packages)\n- [User Interface](#user-interface)\n- [Semantic versioning](#semantic-versioning)\n- [Version Life Cycle](#version-life-cycle)\n- [Support for Python and Kubernetes versions](#support-for-python-and-kubernetes-versions)\n- [Base OS support for reference Airflow images](#base-os-support-for-reference-airflow-images)\n- [Approach to dependencies of Airflow](#approach-to-dependencies-of-airflow)\n- [Support for providers](#support-for-providers)\n- [Contributing](#contributing)\n- [Who uses Apache Airflow?](#who-uses-apache-airflow)\n- [Who Maintains Apache Airflow?](#who-maintains-apache-airflow)\n- [Can I use the Apache Airflow logo in my presentation?](#can-i-use-the-apache-airflow-logo-in-my-presentation)\n- [Airflow merchandise](#airflow-merchandise)\n- [Links](#links)\n- [Sponsors](#sponsors)\n\n\n\n## Project Focus\n\nAirflow works best with workflows that are mostly static and slowly changing. When the DAG structure is similar from one run to the next, it clarifies the unit of work and continuity. Other similar projects include [Luigi](https://github.com/spotify/luigi), [Oozie](https://oozie.apache.org/) and [Azkaban](https://azkaban.github.io/).\n\nAirflow is commonly used to process data, but has the opinion that tasks should ideally be idempotent (i.e., results of the task will be the same, and will not create duplicated data in a destination system), and should not pass large quantities of data from one task to the next (though tasks can pass metadata using Airflow's [Xcom feature](https://airflow.apache.org/docs/apache-airflow/stable/concepts.html#xcoms)). For high-volume, data-intensive tasks, a best practice is to delegate to external services specializing in that type of work.\n\nAirflow is not a streaming solution, but it is often used to process real-time data, pulling data off streams in batches.\n\n## Principles\n\n- **Dynamic**: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically.\n- **Extensible**: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.\n- **Elegant**: Airflow pipelines are lean and explicit. Parameterizing your scripts is built into the core of Airflow using the powerful **Jinja** templating engine.\n- **Scalable**: Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers.\n\n## Requirements\n\nApache Airflow is tested with:\n\n| | Main version (dev) | Stable version (2.3.0) |\n|---------------------|-------------------------|--------------------------|\n| Python | 3.7, 3.8, 3.9, 3.10 | 3.7, 3.8, 3.9, 3.10 |\n| Platform | AMD64/ARM64(\\*) | AMD64/ARM64(\\*) |\n| Kubernetes | 1.20, 1.21, 1.22, 1.23 | 1.20, 1.21, 1.22, 1.23 |\n| PostgreSQL | 10, 11, 12, 13 | 10, 11, 12, 13 |\n| MySQL | 5.7, 8 | 5.7, 8 |\n| SQLite | 3.15.0+ | 3.15.0+ |\n| MSSQL | 2017(\\*), 2019 (\\*) | 2017(\\*), 2019 (\\*) |\n\n\\* Experimental\n\n**Note**: MySQL 5.x versions are unable to or have limitations with\nrunning multiple schedulers -- please see the [Scheduler docs](https://airflow.apache.org/docs/apache-airflow/stable/scheduler.html).\nMariaDB is not tested/recommended.\n\n**Note**: SQLite is used in Airflow tests. Do not use it in production. We recommend\nusing the latest stable version of SQLite for local development.\n\n**Note**: Support for Python v3.10 will be available from Airflow 2.3.0. The `main` (development) branch\nalready supports Python 3.10.\n\n**Note**: Airflow currently can be run on POSIX-compliant Operating Systems. For development it is regularly\ntested on fairly modern Linux Distros and recent versions of MacOS.\nOn Windows you can run it via WSL2 (Windows Subsystem for Linux 2) or via Linux Containers.\nThe work to add Windows support is tracked via [#10388](https://github.com/apache/airflow/issues/10388) but\nit is not a high priority. You should only use Linux-based distros as \"Production\" execution environment\nas this is the only environment that is supported. The only distro that is used in our CI tests and that\nis used in the [Community managed DockerHub image](https://hub.docker.com/p/apache/airflow) is\n`Debian Bullseye`.\n\n## Getting started\n\nVisit the official Airflow website documentation (latest **stable** release) for help with\n[installing Airflow](https://airflow.apache.org/docs/apache-airflow/stable/installation.html),\n[getting started](https://airflow.apache.org/docs/apache-airflow/stable/start/index.html), or walking\nthrough a more complete [tutorial](https://airflow.apache.org/docs/apache-airflow/stable/tutorial.html).\n\n> Note: If you're looking for documentation for the main branch (latest development branch): you can find it on [s.apache.org/airflow-docs](https://s.apache.org/airflow-docs/).\n\nFor more information on Airflow Improvement Proposals (AIPs), visit\nthe [Airflow Wiki](https://cwiki.apache.org/confluence/display/AIRFLOW/Airflow+Improvements+Proposals).\n\nDocumentation for dependent projects like provider packages, Docker image, Helm Chart, you'll find it in [the documentation index](https://airflow.apache.org/docs/).\n\n## Installing from PyPI\n\nWe publish Apache Airflow as `apache-airflow` package in PyPI. Installing it however might be sometimes tricky\nbecause Airflow is a bit of both a library and application. Libraries usually keep their dependencies open, and\napplications usually pin them, but we should do neither and both simultaneously. We decided to keep\nour dependencies as open as possible (in `setup.py`) so users can install different versions of libraries\nif needed. This means that `pip install apache-airflow` will not work from time to time or will\nproduce unusable Airflow installation.\n\nTo have repeatable installation, however, we keep a set of \"known-to-be-working\" constraint\nfiles in the orphan `constraints-main` and `constraints-2-0` branches. We keep those \"known-to-be-working\"\nconstraints files separately per major/minor Python version.\nYou can use them as constraint files when installing Airflow from PyPI. Note that you have to specify\ncorrect Airflow tag/version/branch and Python versions in the URL.\n\n\n1. Installing just Airflow:\n\n> Note: Only `pip` installation is currently officially supported.\n\nWhile it is possible to install Airflow with tools like [Poetry](https://python-poetry.org) or\n[pip-tools](https://pypi.org/project/pip-tools), they do not share the same workflow as\n`pip` - especially when it comes to constraint vs. requirements management.\nInstalling via `Poetry` or `pip-tools` is not currently supported.\n\nIf you wish to install Airflow using those tools, you should use the constraint files and convert\nthem to the appropriate format and workflow that your tool requires.\n\n\n```bash\npip install 'apache-airflow==2.3.0' \\\n --constraint \"https://raw.githubusercontent.com/apache/airflow/constraints-2.3.0/constraints-3.7.txt\"\n```\n\n2. Installing with extras (i.e., postgres, google)\n\n```bash\npip install 'apache-airflow[postgres,google]==2.3.0' \\\n --constraint \"https://raw.githubusercontent.com/apache/airflow/constraints-2.3.0/constraints-3.7.txt\"\n```\n\nFor information on installing provider packages, check\n[providers](http://airflow.apache.org/docs/apache-airflow-providers/index.html).\n\n## Official source code\n\nApache Airflow is an [Apache Software Foundation](https://www.apache.org) (ASF) project,\nand our official source code releases:\n\n- Follow the [ASF Release Policy](https://www.apache.org/legal/release-policy.html)\n- Can be downloaded from [the ASF Distribution Directory](https://downloads.apache.org/airflow)\n- Are cryptographically signed by the release manager\n- Are officially voted on by the PMC members during the\n [Release Approval Process](https://www.apache.org/legal/release-policy.html#release-approval)\n\nFollowing the ASF rules, the source packages released must be sufficient for a user to build and test the\nrelease provided they have access to the appropriate platform and tools.\n\n## Convenience packages\n\nThere are other ways of installing and using Airflow. Those are \"convenience\" methods - they are\nnot \"official releases\" as stated by the `ASF Release Policy`, but they can be used by the users\nwho do not want to build the software themselves.\n\nThose are - in the order of most common ways people install Airflow:\n\n- [PyPI releases](https://pypi.org/project/apache-airflow/) to install Airflow using standard `pip` tool\n- [Docker Images](https://hub.docker.com/r/apache/airflow) to install airflow via\n `docker` tool, use them in Kubernetes, Helm Charts, `docker-compose`, `docker swarm`, etc. You can\n read more about using, customising, and extending the images in the\n [Latest docs](https://airflow.apache.org/docs/docker-stack/index.html), and\n learn details on the internals in the [IMAGES.rst](https://github.com/apache/airflow/blob/main/IMAGES.rst) document.\n- [Tags in GitHub](https://github.com/apache/airflow/tags) to retrieve the git project sources that\n were used to generate official source packages via git\n\nAll those artifacts are not official releases, but they are prepared using officially released sources.\nSome of those artifacts are \"development\" or \"pre-release\" ones, and they are clearly marked as such\nfollowing the ASF Policy.\n\n## User Interface\n\n- **DAGs**: Overview of all DAGs in your environment.\n\n ![DAGs](https://raw.githubusercontent.com/apache/airflow/main/docs/apache-airflow/img/dags.png)\n\n- **Grid**: Grid representation of a DAG that spans across time.\n\n ![Grid](https://raw.githubusercontent.com/apache/airflow/main/docs/apache-airflow/img/grid.png)\n\n- **Graph**: Visualization of a DAG's dependencies and their current status for a specific run.\n\n ![Graph](https://raw.githubusercontent.com/apache/airflow/main/docs/apache-airflow/img/graph.png)\n\n- **Task Duration**: Total time spent on different tasks over time.\n\n ![Task Duration](https://raw.githubusercontent.com/apache/airflow/main/docs/apache-airflow/img/duration.png)\n\n- **Gantt**: Duration and overlap of a DAG.\n\n ![Gantt](https://raw.githubusercontent.com/apache/airflow/main/docs/apache-airflow/img/gantt.png)\n\n- **Code**: Quick way to view source code of a DAG.\n\n ![Code](https://raw.githubusercontent.com/apache/airflow/main/docs/apache-airflow/img/code.png)\n\n## Semantic versioning\n\nAs of Airflow 2.0.0, we support a strict [SemVer](https://semver.org/) approach for all packages released.\n\nThere are few specific rules that we agreed to that define details of versioning of the different\npackages:\n\n* **Airflow**: SemVer rules apply to core airflow only (excludes any changes to providers).\n Changing limits for versions of Airflow dependencies is not a breaking change on its own.\n* **Airflow Providers**: SemVer rules apply to changes in the particular provider's code only.\n SemVer MAJOR and MINOR versions for the packages are independent of the Airflow version.\n For example, `google 4.1.0` and `amazon 3.0.3` providers can happily be installed\n with `Airflow 2.1.2`. If there are limits of cross-dependencies between providers and Airflow packages,\n they are present in providers as `install_requires` limitations. We aim to keep backwards\n compatibility of providers with all previously released Airflow 2 versions but\n there will sometimes be breaking changes that might make some, or all\n providers, have minimum Airflow version specified. Change of that minimum supported Airflow version\n is a breaking change for provider because installing the new provider might automatically\n upgrade Airflow (which might be an undesired side effect of upgrading provider).\n* **Airflow Helm Chart**: SemVer rules apply to changes in the chart only. SemVer MAJOR and MINOR\n versions for the chart are independent from the Airflow version. We aim to keep backwards\n compatibility of the Helm Chart with all released Airflow 2 versions, but some new features might\n only work starting from specific Airflow releases. We might however limit the Helm\n Chart to depend on minimal Airflow version.\n* **Airflow API clients**: SemVer MAJOR and MINOR versions follow MAJOR and MINOR versions of Airflow.\n The first MAJOR or MINOR X.Y.0 release of Airflow should always be followed by X.Y.0 release of\n all clients. The clients then can release their own PATCH releases with bugfixes,\n independently of Airflow PATCH releases.\n\n## Version Life Cycle\n\nApache Airflow version life cycle:\n\n\n\n\n| Version | Current Patch/Minor | State | First Release | Limited Support | EOL/Terminated |\n|-----------|-----------------------|-----------|-----------------|-------------------|------------------|\n| 2 | 2.3.0 | Supported | Dec 17, 2020 | TBD | TBD |\n| 1.10 | 1.10.15 | EOL | Aug 27, 2018 | Dec 17, 2020 | June 17, 2021 |\n| 1.9 | 1.9.0 | EOL | Jan 03, 2018 | Aug 27, 2018 | Aug 27, 2018 |\n| 1.8 | 1.8.2 | EOL | Mar 19, 2017 | Jan 03, 2018 | Jan 03, 2018 |\n| 1.7 | 1.7.1.2 | EOL | Mar 28, 2016 | Mar 19, 2017 | Mar 19, 2017 |\n\n\n\nLimited support versions will be supported with security and critical bug fix only.\nEOL versions will not get any fixes nor support.\nWe always recommend that all users run the latest available minor release for whatever major version is in use.\nWe **highly** recommend upgrading to the latest Airflow major release at the earliest convenient time and before the EOL date.\n\n## Support for Python and Kubernetes versions\n\nAs of Airflow 2.0, we agreed to certain rules we follow for Python and Kubernetes support.\nThey are based on the official release schedule of Python and Kubernetes, nicely summarized in the\n[Python Developer's Guide](https://devguide.python.org/#status-of-python-branches) and\n[Kubernetes version skew policy](https://kubernetes.io/docs/setup/release/version-skew-policy/).\n\n1. We drop support for Python and Kubernetes versions when they reach EOL. Except for kubernetes, a\n version stay supported by Airflow if two major cloud provider still provide support for it. We drop\n support for those EOL versions in main right after EOL date, and it is effectively removed when we release\n the first new MINOR (Or MAJOR if there is no new MINOR version) of Airflow For example, for Python 3.7 it\n means that we will drop support in main right after 27.06.2023, and the first MAJOR or MINOR version of\n Airflow released after will not have it.\n\n2. The \"oldest\" supported version of Python/Kubernetes is the default one until we decide to switch to\n later version. \"Default\" is only meaningful in terms of \"smoke tests\" in CI PRs, which are run using this\n default version and the default reference image available. Currently `apache/airflow:latest`\n and `apache/airflow:2.3.0` images are Python 3.7 images. This means that default reference image will\n become the default at the time when we start preparing for dropping 3.7 support which is few months\n before the end of life for Python 3.7.\n\n3. We support a new version of Python/Kubernetes in main after they are officially released, as soon as we\n make them work in our CI pipeline (which might not be immediate due to dependencies catching up with\n new versions of Python mostly) we release new images/support in Airflow based on the working CI setup.\n\n## Base OS support for reference Airflow images\n\nThe Airflow Community provides conveniently packaged container images that are published whenever\nwe publish an Apache Airflow release. Those images contain:\n\n* Base OS with necessary packages to install Airflow (stable Debian OS)\n* Base Python installation in versions supported at the time of release for the MINOR version of\n Airflow released (so there could be different versions for 2.3 and 2.2 line for example)\n* Libraries required to connect to suppoerted Databases (again the set of databases supported depends\n on the MINOR version of Airflow.\n* Predefined set of popular providers (for details see the [Dockerfile](Dockerfile)).\n* Possibility of building your own, custom image where the user can choose their own set of providers\n and libraries (see [Building the image](https://airflow.apache.org/docs/docker-stack/build.html))\n* In the future Airflow might also support a \"slim\" version without providers nor database clients installed\n\nThe version of the base OS image is the stable version of Debian. Airflow supports using all currently active\nstable versions - as soon as all Airflow dependencies support building, and we set up the CI pipeline for\nbuilding and testing the OS version. Approximately 6 months before the end-of-life of a previous stable\nversion of the OS, Airflow switches the images released to use the latest supported version of the OS.\nFor example since Debian Buster end-of-life is August 2022, Airflow switches the images in `main` branch\nto use Debian Bullseye in February/March 2022. The version will be used in the next MINOR release after\nthe switch happens. In case of the Bullseye switch - 2.3.0 version will use Bullseye. The images released\nin the previous MINOR version continue to use the version that all other releases for the MINOR version\nused.\n\nUsers will continue to be able to build their images using stable Debian releases until the end of life and\nbuilding and verifying of the images happens in our CI but no unit tests are executed using this image in\nthe `main` branch.\n\n## Approach to dependencies of Airflow\n\nAirflow has a lot of dependencies - direct and transitive, also Airflow is both - library and application,\ntherefore our policies to dependencies has to include both - stability of installation of application,\nbut also ability to install newer version of dependencies for those users who develop DAGs. We developed\nthe approach where `constraints` are used to make sure airflow can be installed in a repeatable way, while\nwe do not limit our users to upgrade most of the dependencies. As a result we decided not to upper-bound\nversion of Airflow dependencies by default, unless we have good reasons to believe upper-bounding them is\nneeded because of importance of the dependency as well as risk it involves to upgrade specific dependency.\nWe also upper-bound the dependencies that we know cause problems.\n\nThe constraint mechanism of ours takes care about finding and upgrading all the non-upper bound dependencies\nautomatically (providing that all the tests pass). Our `main` build failures will indicate in case there\nare versions of dependencies that break our tests - indicating that we should either upper-bind them or\nthat we should fix our code/tests to account for the upstream changes from those dependencies.\n\nWhenever we upper-bound such a dependency, we should always comment why we are doing it - i.e. we should have\na good reason why dependency is upper-bound. And we should also mention what is the condition to remove the\nbinding.\n\n### Approach for dependencies for Airflow Core\n\nThose `extras` and `providers` dependencies are maintained in `setup.cfg`.\n\nThere are few dependencies that we decided are important enough to upper-bound them by default, as they are\nknown to follow predictable versioning scheme, and we know that new versions of those are very likely to\nbring breaking changes. We commit to regularly review and attempt to upgrade to the newer versions of\nthe dependencies as they are released, but this is manual process.\n\nThe important dependencies are:\n\n* `SQLAlchemy`: upper-bound to specific MINOR version (SQLAlchemy is known to remove deprecations and\n introduce breaking changes especially that support for different Databases varies and changes at\n various speed (example: SQLAlchemy 1.4 broke MSSQL integration for Airflow)\n* `Alembic`: it is important to handle our migrations in predictable and performant way. It is developed\n together with SQLAlchemy. Our experience with Alembic is that it very stable in MINOR version\n* `Flask`: We are using Flask as the back-bone of our web UI and API. We know major version of Flask\n are very likely to introduce breaking changes across those so limiting it to MAJOR version makes sense\n* `werkzeug`: the library is known to cause problems in new versions. It is tightly coupled with Flask\n libraries, and we should update them together\n* `celery`: Celery is crucial component of Airflow as it used for CeleryExecutor (and similar). Celery\n [follows SemVer](https://docs.celeryq.dev/en/stable/contributing.html?highlight=semver#versions), so\n we should upper-bound it to the next MAJOR version. Also when we bump the upper version of the library,\n we should make sure Celery Provider minimum Airflow version is updated).\n* `kubernetes`: Kubernetes is a crucial component of Airflow as it is used for the KubernetesExecutor\n (and similar). Kubernetes Python library [follows SemVer](https://github.com/kubernetes-client/python#compatibility),\n so we should upper-bound it to the next MAJOR version. Also when we bump the upper version of the library,\n we should make sure Kubernetes Provider minimum Airflow version is updated.\n\n### Approach for dependencies in Airflow Providers and extras\n\nThose `extras` and `providers` dependencies are maintained in `setup.py`.\n\nBy default, we should not upper-bound dependencies for providers, however each provider's maintainer\nmight decide to add additional limits (and justify them with comment)\n\n## Support for providers\n\nProviders released by the community have limitation of a minimum supported version of Airflow. The minimum\nversion of Airflow is the `MINOR` version (2.1, 2.2 etc.) indicating that the providers might use features\nthat appeared in this release. The default support timespan for the minimum version of Airflow\n(there could be justified exceptions) is that we increase the minimum Airflow version, when 12 months passed\nsince the first release for the MINOR version of Airflow.\n\nFor example this means that by default we upgrade the minimum version of Airflow supported by providers\nto 2.2.0 in the first Provider's release after 21st of May 2022 (21st of May 2021 is the date when the\nfirst `PATCHLEVEL` of 2.1 (2.1.0) has been released.\n\n## Contributing\n\nWant to help build Apache Airflow? Check out our [contributing documentation](https://github.com/apache/airflow/blob/main/CONTRIBUTING.rst).\n\nOfficial Docker (container) images for Apache Airflow are described in [IMAGES.rst](https://github.com/apache/airflow/blob/main/IMAGES.rst).\n\n## Who uses Apache Airflow?\n\nMore than 400 organizations are using Apache Airflow\n[in the wild](https://github.com/apache/airflow/blob/main/INTHEWILD.md).\n\n## Who Maintains Apache Airflow?\n\nAirflow is the work of the [community](https://github.com/apache/airflow/graphs/contributors),\nbut the [core committers/maintainers](https://people.apache.org/committers-by-project.html#airflow)\nare responsible for reviewing and merging PRs as well as steering conversations around new feature requests.\nIf you would like to become a maintainer, please review the Apache Airflow\n[committer requirements](https://github.com/apache/airflow/blob/main/COMMITTERS.rst#guidelines-to-become-an-airflow-committer).\n\n## Can I use the Apache Airflow logo in my presentation?\n\nYes! Be sure to abide by the Apache Foundation [trademark policies](https://www.apache.org/foundation/marks/#books) and the Apache Airflow [Brandbook](https://cwiki.apache.org/confluence/display/AIRFLOW/Brandbook). The most up to date logos are found in [this repo](/docs/apache-airflow/img/logos) and on the Apache Software Foundation [website](https://www.apache.org/logos/about.html).\n\n## Airflow merchandise\n\nIf you would love to have Apache Airflow stickers, t-shirt, etc. then check out\n[Redbubble Shop](https://www.redbubble.com/i/sticker/Apache-Airflow-by-comdev/40497530.EJUG5).\n\n## Links\n\n- [Documentation](https://airflow.apache.org/docs/apache-airflow/stable/)\n- [Chat](https://s.apache.org/airflow-slack)\n\n## Sponsors\n\nThe CI infrastructure for Apache Airflow has been sponsored by:\n\n\n\n\"astronomer.io\"\n\"AWS\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "https://archive.apache.org/dist/airflow/2.3.0", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://airflow.apache.org/", "keywords": "", "license": "Apache License 2.0", "maintainer": "", "maintainer_email": "", "name": "apache-airflow", 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