This project forked from apache/airflow
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Home Page: https://airflow.apache.org/
License: Apache License 2.0
watchtower001110> 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. --> <!-- START Apache Airflow, please keep comment here to allow auto update of PyPI readme.md --> # Apache Airflow [](https://badge.fury.io/py/apache-airflow) [](https://github.com/apache/airflow/actions) [](https://codecov.io/gh/apache/airflow) [](https://www.apache.org/licenses/LICENSE-2.0.txt) [](https://pypi.org/project/apache-airflow/) [](https://hub.docker.com/r/apache/airflow) [](https://hub.docker.com/r/apache/airflow) [](https://pypi.org/project/apache-airflow/) [](https://artifacthub.io/packages/search?repo=apache-airflow) [](https://github.com/psf/black) [](https://twitter.com/ApacheAirflow) [](https://s.apache.org/airflow-slack) [](https://github.com/apache/airflow/graphs/contributors) [](https://ossrank.com/p/6) [Apache Airflow](https://airflow.apache.org/docs/apache-airflow/stable/) (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. <!-- END Apache Airflow, please keep comment here to allow auto update of PyPI readme.md --> <!-- START doctoc generated TOC please keep comment here to allow auto update --> <!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE --> **Table of contents** - [Project Focus](#project-focus) - [Principles](#principles) - [Requirements](#requirements) - [Getting started](#getting-started) - [Installing from PyPI](#installing-from-pypi) - [Official source code](#official-source-code) - [Convenience packages](#convenience-packages) - [User Interface](#user-interface) - [Semantic versioning](#semantic-versioning) - [Version Life Cycle](#version-life-cycle) - [Support for Python and Kubernetes versions](#support-for-python-and-kubernetes-versions) - [Base OS support for reference Airflow images](#base-os-support-for-reference-airflow-images) - [Approach to dependencies of Airflow](#approach-to-dependencies-of-airflow) - [Contributing](#contributing) - [Who uses Apache Airflow?](#who-uses-apache-airflow) - [Who maintains Apache Airflow?](#who-maintains-apache-airflow) - [Can I use the Apache Airflow logo in my presentation?](#can-i-use-the-apache-airflow-logo-in-my-presentation) - [Airflow merchandise](#airflow-merchandise) - [Links](#links) - [Sponsors](#sponsors) <!-- END doctoc generated TOC please keep comment here to allow auto update --> ## Project Focus Airflow 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/). Airflow 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/xcoms.html)). For high-volume, data-intensive tasks, a best practice is to delegate to external services specializing in that type of work. Airflow is not a streaming solution, but it is often used to process real-time data, pulling data off streams in batches. ## 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. <!-- START Requirements, please keep comment here to allow auto update of PyPI readme.md --> ## Requirements Apache Airflow is tested with: | | Main version (dev) | Stable version (2.7.1) | |-------------|------------------------------|------------------------| | Python | 3.8, 3.9, 3.10, 3.11 | 3.8, 3.9, 3.10, 3.11 | | Platform | AMD64/ARM64(\*) | AMD64/ARM64(\*) | | Kubernetes | 1.24, 1.25, 1.26, 1.27, 1.28 | 1.24, 1.25, 1.26, 1.27 | | PostgreSQL | 11, 12, 13, 14, 15 | 11, 12, 13, 14, 15 | | MySQL | 5.7, 8.0, 8.1 | 5.7, 8.0 | | SQLite | 3.15.0+ | 3.15.0+ | | MSSQL | 2017(\*), 2019(\*) | 2017(\*), 2019(\*) | \* Experimental **Note**: MySQL 5.x versions are unable to or have limitations with running multiple schedulers -- please see the [Scheduler docs](https://airflow.apache.org/docs/apache-airflow/stable/administration-and-deployment/scheduler.html). MariaDB is not tested/recommended. **Note**: SQLite is used in Airflow tests. Do not use it in production. We recommend using the latest stable version of SQLite for local development. **Note**: Airflow currently can be run on POSIX-compliant Operating Systems. For development, it is regularly tested on fairly modern Linux Distros and recent versions of macOS. On Windows you can run it via WSL2 (Windows Subsystem for Linux 2) or via Linux Containers. The work to add Windows support is tracked via [#10388](apache#10388), but it is not a high priority. You should only use Linux-based distros as "Production" execution environment as this is the only environment that is supported. The only distro that is used in our CI tests and that is used in the [Community managed DockerHub image](https://hub.docker.com/p/apache/airflow) is `Debian Bullseye`. <!-- END Requirements, please keep comment here to allow auto update of PyPI readme.md --> <!-- START Getting started, please keep comment here to allow auto update of PyPI readme.md --> ## Getting started Visit the official Airflow website documentation (latest **stable** release) for help with [installing Airflow](https://airflow.apache.org/docs/apache-airflow/stable/installation/), [getting started](https://airflow.apache.org/docs/apache-airflow/stable/start.html), or walking through a more complete [tutorial](https://airflow.apache.org/docs/apache-airflow/stable/tutorial/). > 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/). For more information on Airflow Improvement Proposals (AIPs), visit the [Airflow Wiki](https://cwiki.apache.org/confluence/display/AIRFLOW/Airflow+Improvement+Proposals). Documentation for dependent projects like provider packages, Docker image, Helm Chart, you'll find it in [the documentation index](https://airflow.apache.org/docs/). <!-- END Getting started, please keep comment here to allow auto update of PyPI readme.md --> <!-- START Installing from PyPI, please keep comment here to allow auto update of PyPI readme.md --> ## Installing from PyPI We publish Apache Airflow as `apache-airflow` package in PyPI. Installing it however might be sometimes tricky because Airflow is a bit of both a library and application. Libraries usually keep their dependencies open, and applications usually pin them, but we should do neither and both simultaneously. We decided to keep our dependencies as open as possible (in `setup.py`) so users can install different versions of libraries if needed. This means that `pip install apache-airflow` will not work from time to time or will produce unusable Airflow installation. To have repeatable installation, however, we keep a set of "known-to-be-working" constraint files in the orphan `constraints-main` and `constraints-2-0` branches. We keep those "known-to-be-working" constraints files separately per major/minor Python version. You can use them as constraint files when installing Airflow from PyPI. Note that you have to specify correct Airflow tag/version/branch and Python versions in the URL. 1. Installing just Airflow: > Note: Only `pip` installation is currently officially supported. While it is possible to install Airflow with tools like [Poetry](https://python-poetry.org) or [pip-tools](https://pypi.org/project/pip-tools), they do not share the same workflow as `pip` - especially when it comes to constraint vs. requirements management. Installing via `Poetry` or `pip-tools` is not currently supported. There are known issues with ``bazel`` that might lead to circular dependencies when using it to install Airflow. Please switch to ``pip`` if you encounter such problems. ``Bazel`` community works on fixing the problem in `this PR <https://github.com/bazelbuild/rules_python/pull/1166>`_ so it might be that newer versions of ``bazel`` will handle it. If you wish to install Airflow using those tools, you should use the constraint files and convert them to the appropriate format and workflow that your tool requires. ```bash pip install 'apache-airflow==2.7.1' \ --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-2.7.1/constraints-3.8.txt" ``` 2. Installing with extras (i.e., postgres, google) ```bash pip install 'apache-airflow[postgres,google]==2.7.1' \ --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-2.7.1/constraints-3.8.txt" ``` For information on installing provider packages, check [providers](http://airflow.apache.org/docs/apache-airflow-providers/index.html). <!-- END Installing from PyPI, please keep comment here to allow auto update of PyPI readme.md --> <!-- START Official source code, please keep comment here to allow auto update of PyPI readme.md --> ## Official source code Apache Airflow is an [Apache Software Foundation](https://www.apache.org) (ASF) project, and our official source code releases: - Follow the [ASF Release Policy](https://www.apache.org/legal/release-policy.html) - Can be downloaded from [the ASF Distribution Directory](https://downloads.apache.org/airflow) - Are cryptographically signed by the release manager - Are officially voted on by the PMC members during the [Release Approval Process](https://www.apache.org/legal/release-policy.html#release-approval) Following the ASF rules, the source packages released must be sufficient for a user to build and test the release provided they have access to the appropriate platform and tools. <!-- END Official source code, please keep comment here to allow auto update of PyPI readme.md --> ## Convenience packages There are other ways of installing and using Airflow. Those are "convenience" methods - they are not "official releases" as stated by the `ASF Release Policy`, but they can be used by the users who do not want to build the software themselves. Those are - in the order of most common ways people install Airflow: - [PyPI releases](https://pypi.org/project/apache-airflow/) to install Airflow using standard `pip` tool - [Docker Images](https://hub.docker.com/r/apache/airflow) to install airflow via `docker` tool, use them in Kubernetes, Helm Charts, `docker-compose`, `docker swarm`, etc. You can read more about using, customising, and extending the images in the [Latest docs](https://airflow.apache.org/docs/docker-stack/index.html), and learn details on the internals in the [IMAGES.rst](https://github.com/apache/airflow/blob/main/IMAGES.rst) document. - [Tags in GitHub](https://github.com/apache/airflow/tags) to retrieve the git project sources that were used to generate official source packages via git All those artifacts are not official releases, but they are prepared using officially released sources. Some of those artifacts are "development" or "pre-release" ones, and they are clearly marked as such following the ASF Policy. ## User Interface - **DAGs**: Overview of all DAGs in your environment.  - **Grid**: Grid representation of a DAG that spans across time.  - **Graph**: 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**: Duration and overlap of a DAG.  - **Code**: Quick way to view source code of a DAG.  ## Semantic versioning As of Airflow 2.0.0, we support a strict [SemVer](https://semver.org/) approach for all packages released. There are few specific rules that we agreed to that define details of versioning of the different packages: * **Airflow**: SemVer rules apply to core airflow only (excludes any changes to providers). Changing limits for versions of Airflow dependencies is not a breaking change on its own. * **Airflow Providers**: SemVer rules apply to changes in the particular provider's code only. SemVer MAJOR and MINOR versions for the packages are independent of the Airflow version. For example, `google 4.1.0` and `amazon 3.0.3` providers can happily be installed with `Airflow 2.1.2`. If there are limits of cross-dependencies between providers and Airflow packages, they are present in providers as `install_requires` limitations. We aim to keep backwards compatibility of providers with all previously released Airflow 2 versions but there will sometimes be breaking changes that might make some, or all providers, have minimum Airflow version specified. Change of that minimum supported Airflow version is a breaking change for provider because installing the new provider might automatically upgrade Airflow (which might be an undesired side effect of upgrading provider). * **Airflow Helm Chart**: SemVer rules apply to changes in the chart only. SemVer MAJOR and MINOR versions for the chart are independent of the Airflow version. We aim to keep backwards compatibility of the Helm Chart with all released Airflow 2 versions, but some new features might only work starting from specific Airflow releases. We might however limit the Helm Chart to depend on minimal Airflow version. * **Airflow API clients**: SemVer MAJOR and MINOR versions follow MAJOR and MINOR versions of Airflow. The first MAJOR or MINOR X.Y.0 release of Airflow should always be followed by X.Y.0 release of all clients. An airflow PATCH X.Y.Z release can be followed by a PATCH release of API clients, only if this PATCH is relevant to the clients. The clients then can release their own PATCH releases with bugfixes, independently of Airflow PATCH releases. As a consequence, each API client will have its own PATCH version that may or may not be in sync with the Airflow PATCH version. For a specific MAJOR/MINOR Airflow version, users should favor the latest PATCH version of clients independently of their Airflow PATCH version. ## Version Life Cycle Apache Airflow version life cycle: <!-- This table is automatically updated by pre-commit scripts/ci/pre_commit/pre_commit_supported_versions.py --> <!-- Beginning of auto-generated table --> | Version | Current Patch/Minor | State | First Release | Limited Support | EOL/Terminated | |-----------|-----------------------|-----------|-----------------|-------------------|------------------| | 2 | 2.7.1 | Supported | Dec 17, 2020 | TBD | TBD | | 1.10 | 1.10.15 | EOL | Aug 27, 2018 | Dec 17, 2020 | June 17, 2021 | | 1.9 | 1.9.0 | EOL | Jan 03, 2018 | Aug 27, 2018 | Aug 27, 2018 | | 1.8 | 1.8.2 | EOL | Mar 19, 2017 | Jan 03, 2018 | Jan 03, 2018 | | 1.7 | 1.7.1.2 | EOL | Mar 28, 2016 | Mar 19, 2017 | Mar 19, 2017 | <!-- End of auto-generated table --> Limited support versions will be supported with security and critical bug fix only. EOL versions will not get any fixes nor support. We always recommend that all users run the latest available minor release for whatever major version is in use. We **highly** recommend upgrading to the latest Airflow major release at the earliest convenient time and before the EOL date. ## Support for Python and Kubernetes versions As of Airflow 2.0, we agreed to certain rules we follow for Python and Kubernetes support. They are based on the official release schedule of Python and Kubernetes, nicely summarized in the [Python Developer's Guide](https://devguide.python.org/#status-of-python-branches) and [Kubernetes version skew policy](https://kubernetes.io/docs/setup/release/version-skew-policy/). 1. We drop support for Python and Kubernetes versions when they reach EOL. Except for Kubernetes, a version stays supported by Airflow if two major cloud providers still provide support for it. We drop support for those EOL versions in main right after EOL date, and it is effectively removed when we release the first new MINOR (Or MAJOR if there is no new MINOR version) of Airflow. For example, for Python 3.8 it means that we will drop support in main right after 27.06.2023, and the first MAJOR or MINOR version of Airflow released after will not have it. 2. We support a new version of Python/Kubernetes in main after they are officially released, as soon as we make them work in our CI pipeline (which might not be immediate due to dependencies catching up with new versions of Python mostly) we release new images/support in Airflow based on the working CI setup. 3. This policy is best-effort which means there may be situations where we might terminate support earlier if circumstances require it. ## Base OS support for reference Airflow images The Airflow Community provides conveniently packaged container images that are published whenever we publish an Apache Airflow release. Those images contain: * Base OS with necessary packages to install Airflow (stable Debian OS) * Base Python installation in versions supported at the time of release for the MINOR version of Airflow released (so there could be different versions for 2.3 and 2.2 line for example) * Libraries required to connect to supported Databases (again the set of databases supported depends on the MINOR version of Airflow) * Predefined set of popular providers (for details see the [Dockerfile](https://raw.githubusercontent.com/apache/airflow/main/Dockerfile)). * Possibility of building your own, custom image where the user can choose their own set of providers and libraries (see [Building the image](https://airflow.apache.org/docs/docker-stack/build.html)) * In the future Airflow might also support a "slim" version without providers nor database clients installed The version of the base OS image is the stable version of Debian. Airflow supports using all currently active stable versions - as soon as all Airflow dependencies support building, and we set up the CI pipeline for building and testing the OS version. Approximately 6 months before the end-of-life of a previous stable version of the OS, Airflow switches the images released to use the latest supported version of the OS. For example since ``Debian Buster`` end-of-life was August 2022, Airflow switched the images in `main` branch to use ``Debian Bullseye`` in February/March 2022. The version was used in the next MINOR release after the switch happened. In case of the Bullseye switch - 2.3.0 version used ``Debian Bullseye``. The images released in the previous MINOR version continue to use the version that all other releases for the MINOR version used. Support for ``Debian Buster`` image was dropped in August 2022 completely and everyone is expected to stop building their images using ``Debian Buster``. Users will continue to be able to build their images using stable Debian releases until the end of life and building and verifying of the images happens in our CI but no unit tests were executed using this image in the `main` branch. ## Approach to dependencies of Airflow Airflow has a lot of dependencies - direct and transitive, also Airflow is both - library and application, therefore our policies to dependencies has to include both - stability of installation of application, but also ability to install newer version of dependencies for those users who develop DAGs. We developed the approach where `constraints` are used to make sure airflow can be installed in a repeatable way, while we do not limit our users to upgrade most of the dependencies. As a result we decided not to upper-bound version of Airflow dependencies by default, unless we have good reasons to believe upper-bounding them is needed because of importance of the dependency as well as risk it involves to upgrade specific dependency. We also upper-bound the dependencies that we know cause problems. The constraint mechanism of ours takes care about finding and upgrading all the non-upper bound dependencies automatically (providing that all the tests pass). Our `main` build failures will indicate in case there are versions of dependencies that break our tests - indicating that we should either upper-bind them or that we should fix our code/tests to account for the upstream changes from those dependencies. Whenever we upper-bound such a dependency, we should always comment why we are doing it - i.e. we should have a good reason why dependency is upper-bound. And we should also mention what is the condition to remove the binding. ### Approach for dependencies for Airflow Core Those `extras` and `providers` dependencies are maintained in `setup.cfg`. There are few dependencies that we decided are important enough to upper-bound them by default, as they are known to follow predictable versioning scheme, and we know that new versions of those are very likely to bring breaking changes. We commit to regularly review and attempt to upgrade to the newer versions of the dependencies as they are released, but this is manual process. The important dependencies are: * `SQLAlchemy`: upper-bound to specific MINOR version (SQLAlchemy is known to remove deprecations and introduce breaking changes especially that support for different Databases varies and changes at various speed (example: SQLAlchemy 1.4 broke MSSQL integration for Airflow) * `Alembic`: it is important to handle our migrations in predictable and performant way. It is developed together with SQLAlchemy. Our experience with Alembic is that it very stable in MINOR version * `Flask`: We are using Flask as the back-bone of our web UI and API. We know major version of Flask are very likely to introduce breaking changes across those so limiting it to MAJOR version makes sense * `werkzeug`: the library is known to cause problems in new versions. It is tightly coupled with Flask libraries, and we should update them together * `celery`: Celery is crucial component of Airflow as it used for CeleryExecutor (and similar). Celery [follows SemVer](https://docs.celeryq.dev/en/stable/contributing.html?highlight=semver#versions), so we should upper-bound it to the next MAJOR version. Also, when we bump the upper version of the library, we should make sure Celery Provider minimum Airflow version is updated. * `kubernetes`: Kubernetes is a crucial component of Airflow as it is used for the KubernetesExecutor (and similar). Kubernetes Python library [follows SemVer](https://github.com/kubernetes-client/python#compatibility), so we should upper-bound it to the next MAJOR version. Also, when we bump the upper version of the library, we should make sure Kubernetes Provider minimum Airflow version is updated. ### Approach for dependencies in Airflow Providers and extras The main part of the Airflow is the Airflow Core, but the power of Airflow also comes from a number of providers that extend the core functionality and are released separately, even if we keep them (for now) in the same monorepo for convenience. You can read more about the providers in the [Providers documentation](https://airflow.apache.org/docs/apache-airflow-providers/index.html). We also have set of policies implemented for maintaining and releasing community-managed providers as well as the approach for community vs. 3rd party providers in the [providers](https://github.com/apache/airflow/blob/main/PROVIDERS.rst) document. Those `extras` and `providers` dependencies are maintained in `provider.yaml` of each provider. By default, we should not upper-bound dependencies for providers, however each provider's maintainer might decide to add additional limits (and justify them with comment). <!-- START Contributing, please keep comment here to allow auto update of PyPI readme.md --> ## Contributing Want to help build Apache Airflow? Check out our [contributing documentation](https://github.com/apache/airflow/blob/main/CONTRIBUTING.rst). Official Docker (container) images for Apache Airflow are described in [IMAGES.rst](https://github.com/apache/airflow/blob/main/IMAGES.rst). <!-- END Contributing, please keep comment here to allow auto update of PyPI readme.md --> <!-- START Who uses Apache Airflow, please keep comment here to allow auto update of PyPI readme.md --> ## Who uses Apache Airflow? More than 400 organizations are using Apache Airflow [in the wild](https://github.com/apache/airflow/blob/main/INTHEWILD.md). <!-- END Who uses Apache Airflow, please keep comment here to allow auto update of PyPI readme.md --> <!-- START Who maintains Apache Airflow, please keep comment here to allow auto update of PyPI readme.md --> ## 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 conversations around new feature requests. If you would like to become a maintainer, please review the Apache Airflow [committer requirements](https://github.com/apache/airflow/blob/main/COMMITTERS.rst#guidelines-to-become-an-airflow-committer). <!-- END Who maintains Apache Airflow, please keep comment here to allow auto update of PyPI readme.md --> ## Can I use the Apache Airflow logo in my presentation? Yes! 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](https://github.com/apache/airflow/tree/main/docs/apache-airflow/img/logos/) and on the Apache Software Foundation [website](https://www.apache.org/logos/about.html). ## Airflow merchandise If you would love to have Apache Airflow stickers, t-shirt, etc. then check out [Redbubble Shop](https://www.redbubble.com/i/sticker/Apache-Airflow-by-comdev/40497530.EJUG5). ## Links - [Documentation](https://airflow.apache.org/docs/apache-airflow/stable/) - [Chat](https://s.apache.org/airflow-slack) ## Sponsors The CI infrastructure for Apache Airflow has been sponsored by: <!-- Ordered by most recently "funded" --> <a href="https://astronomer.io"><img src="https://assets2.astronomer.io/logos/logoForLIGHTbackground.png" alt="astronomer.io" width="250px"></a> <a href="https://aws.amazon.com/opensource/"><img src="docs/integration-logos/aws/[email protected]" alt="AWS OpenSource" width="130px"></a>
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
Django
The Web framework for perfectionists with deadlines.
Laravel
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
Microsoft
Open source projects and samples from Microsoft.
Google
Google ❤️ Open Source for everyone.
Alibaba
Alibaba Open Source for everyone
D3
Data-Driven Documents codes.
Tencent
China tencent open source team.
](https://badge.fury.io/py/apache-airflow){kind=link}
](https://github.com/apache/airflow/actions){kind=link}
](https://codecov.io/gh/apache/airflow){kind=link}
](https://www.apache.org/licenses/LICENSE-2.0.txt){kind=link}
](https://pypi.org/project/apache-airflow/){kind=link}
](https://hub.docker.com/r/apache/airflow){kind=link}
](https://hub.docker.com/r/apache/airflow){kind=link}
](https://github.com/psf/black){kind=link}
](https://twitter.com/ApacheAirflow){kind=link}
](https://s.apache.org/airflow-slack){kind=link}
