[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9659":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":34,"readmeContent":35,"aiSummary":36,"trendingCount":16,"starSnapshotCount":16,"syncStatus":37,"lastSyncTime":38,"discoverSource":39},9659,"kedro","kedro-org\u002Fkedro","kedro-org","Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.","https:\u002F\u002Fkedro.org",null,"Python",10886,1040,100,151,0,1,14,31,7,81.15,"Apache License 2.0",false,"main",true,[27,28,5,29,30,31,32,33],"experiment-tracking","hacktoberfest","machine-learning","machine-learning-engineering","mlops","pipeline","python","2026-06-12 04:00:46","\u003Cp align=\"center\">\n \u003Cpicture>\n \u003Csource media=\"(prefers-color-scheme: light)\" srcset=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fkedro-org\u002Fkedro\u002Fmain\u002F.github\u002Fdemo-light.png\">\n \u003Csource media=\"(prefers-color-scheme: dark)\" srcset=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fkedro-org\u002Fkedro\u002Fmain\u002F.github\u002Fdemo-dark.png\">\n \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fkedro-org\u002Fkedro\u002Fmain\u002F.github\u002Fdemo-light.png\" alt=\"Kedro\">\n \u003C\u002Fpicture>\n\u003C\u002Fp>\n\n[![Python version](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fpython-3.10%20%7C%203.11%20%7C%203.12%20%7C%203.13%20%7C%203.14-blue.svg)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fkedro\u002F)\n[![PyPI version](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Fkedro.svg)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fkedro\u002F)\n[![Conda version](https:\u002F\u002Fimg.shields.io\u002Fconda\u002Fvn\u002Fconda-forge\u002Fkedro.svg)](https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fkedro)\n[![License](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flicense-Apache%202.0-blue.svg)](https:\u002F\u002Fgithub.com\u002Fkedro-org\u002Fkedro\u002Fblob\u002Fmain\u002FLICENSE.md)\n[![Slack Organisation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fslack-chat-blueviolet.svg?label=Kedro%20Slack&logo=slack)](https:\u002F\u002Fslack.kedro.org)\n[![Slack Archive](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fslack-archive-blueviolet.svg?label=Kedro%20Slack%20)](https:\u002F\u002Flinen-slack.kedro.org\u002F)\n![GitHub Actions Workflow Status - Main](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Factions\u002Fworkflow\u002Fstatus\u002Fkedro-org\u002Fkedro\u002Fall-checks.yml?label=main)\n![GitHub Actions Workflow Status - Develop](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Factions\u002Fworkflow\u002Fstatus\u002Fkedro-org\u002Fkedro\u002Fall-checks.yml?branch=develop&label=develop)\n[![Documentation](https:\u002F\u002Freadthedocs.org\u002Fprojects\u002Fkedro\u002Fbadge\u002F?version=stable)](https:\u002F\u002Fdocs.kedro.org\u002F)\n[![OpenSSF Best Practices](https:\u002F\u002Fbestpractices.coreinfrastructure.org\u002Fprojects\u002F6711\u002Fbadge)](https:\u002F\u002Fbestpractices.coreinfrastructure.org\u002Fprojects\u002F6711)\n[![Monthly downloads](https:\u002F\u002Fstatic.pepy.tech\u002Fbadge\u002Fkedro\u002Fmonth)](https:\u002F\u002Fpepy.tech\u002Fproject\u002Fkedro)\n[![Total downloads](https:\u002F\u002Fstatic.pepy.tech\u002Fbadge\u002Fkedro)](https:\u002F\u002Fpepy.tech\u002Fproject\u002Fkedro)\n\n[![Powered by Kedro](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fpowered_by-kedro-ffc900?logo=kedro)](https:\u002F\u002Fkedro.org)\n\n## What is Kedro?\n\nKedro is a toolbox for production-ready data engineering and data science pipelines. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular. You can find out more at [kedro.org](https:\u002F\u002Fkedro.org).\n\nKedro is an open-source Python framework hosted by the [LF AI & Data Foundation](https:\u002F\u002Flfaidata.foundation\u002F).\n\n## How do I install Kedro?\n\nTo install Kedro from the Python Package Index (PyPI) run:\n\n```\nuv pip install kedro\n```\n\nIt is also possible to install Kedro using `conda`:\n\n```\nconda install -c conda-forge kedro\n```\n\nOur [Get Started guide](https:\u002F\u002Fdocs.kedro.org\u002Fen\u002Fstable\u002Fgetting-started\u002Finstall\u002F) contains full installation instructions, and includes how to set up Python virtual environments.\n\n### Installation from source\nTo access the latest Kedro version before its official release, install it from the `main` branch.\n```\nuv pip install git+https:\u002F\u002Fgithub.com\u002Fkedro-org\u002Fkedro@main\n```\n\n## What are the main features of Kedro?\n\n| Feature | What is this? |\n| -------------------- |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| Project Template | A standard, modifiable and easy-to-use project template based on [Cookiecutter Data Science](https:\u002F\u002Fgithub.com\u002Fdrivendata\u002Fcookiecutter-data-science\u002F). |\n| Data Catalog | A series of lightweight data connectors used to save and load data across many different file formats and file systems, including local and network file systems, cloud object stores, and HDFS. The Data Catalog also includes data and model versioning for file-based systems. |\n| Pipeline Abstraction | Automatic resolution of dependencies between pure Python functions and data pipeline visualisation using [Kedro-Viz](https:\u002F\u002Fgithub.com\u002Fkedro-org\u002Fkedro-viz). |\n| Coding Standards | Test-driven development using [`pytest`](https:\u002F\u002Fgithub.com\u002Fpytest-dev\u002Fpytest), produce well-documented code using [Sphinx](http:\u002F\u002Fwww.sphinx-doc.org\u002Fen\u002Fmaster\u002F), create linted code with support for [`ruff`](https:\u002F\u002Fgithub.com\u002Fastral-sh\u002Fruff) and make use of the standard Python logging library. |\n| Flexible Deployment | Deployment strategies that include single or distributed-machine deployment as well as additional support for deploying on Argo, Prefect, Kubeflow, AWS Batch, and Databricks. |\n\n## How do I use Kedro?\n\nThe [Kedro documentation](https:\u002F\u002Fdocs.kedro.org\u002Fen\u002Fstable\u002F) first explains [how to install Kedro](https:\u002F\u002Fdocs.kedro.org\u002Fen\u002Fstable\u002Fgetting-started\u002Finstall\u002F) and then introduces [key Kedro concepts](https:\u002F\u002Fdocs.kedro.org\u002Fen\u002Fstable\u002Fgetting-started\u002Fkedro_concepts\u002F\n).\n\nYou can then review the [spaceflights tutorial](https:\u002F\u002Fdocs.kedro.org\u002Fen\u002Fstable\u002Ftutorials\u002Fspaceflights_tutorial\u002F) to build a Kedro project for hands-on experience.\n\nFor new and intermediate Kedro users, there's a comprehensive section on [how to visualise Kedro projects using Kedro-Viz](https:\u002F\u002Fdocs.kedro.org\u002Fprojects\u002Fkedro-viz\u002F).\n\n\n\u003Cp align=\"center\">\n \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fkedro-org\u002Fkedro-viz\u002Fmain\u002F.github\u002Fimg\u002Fbanner.png\" alt>\n \u003Cem>A pipeline visualisation generated using Kedro-Viz\u003C\u002Fem>\n\u003C\u002Fp>\n\nAdditional documentation explains [how to work with Kedro and Jupyter notebooks](https:\u002F\u002Fdocs.kedro.org\u002Fen\u002Fstable\u002Fintegrations-and-plugins\u002Fnotebooks_and_ipython\u002F), and there are a set of advanced user guides for advanced for key Kedro features. We also recommend the [API reference documentation](\u002Fkedro) for further information.\n\n\n## Why does Kedro exist?\n\nKedro is built upon our collective best-practice (and mistakes) trying to deliver real-world ML applications that have vast amounts of raw unvetted data. We developed Kedro to achieve the following:\n\n- To address the main shortcomings of Jupyter notebooks, one-off scripts, and glue-code because there is a focus on\n creating **maintainable data engineering and data science code**\n- To enhance **team collaboration** when different team members have varied exposure to software engineering concepts\n- To increase efficiency, because applied concepts like modularity and separation of concerns inspire the creation of\n **reusable analytics code**\n\nFind out more about how Kedro can answer your use cases from the [product FAQs on the Kedro website](https:\u002F\u002Fkedro.org\u002F#faq).\n\n## The humans behind Kedro\n\nThe [Kedro product team](https:\u002F\u002Fdocs.kedro.org\u002Fen\u002Fstable\u002Fabout\u002Ftechnical_steering_committee\u002F#current-maintainers) and a number of [open source contributors from across the world](https:\u002F\u002Fgithub.com\u002Fkedro-org\u002Fkedro\u002Freleases) maintain Kedro.\n\n## Can I contribute?\n\nYes! We welcome all kinds of contributions. Check out our [guide to contributing to Kedro](https:\u002F\u002Fgithub.com\u002Fkedro-org\u002Fkedro\u002Fwiki\u002FContribute-to-Kedro).\n\n## Where can I learn more?\n\nThere is a growing community around Kedro. We encourage you to ask and answer technical questions on [Slack](https:\u002F\u002Fslack.kedro.org\u002F) and bookmark the [Linen archive of past discussions](https:\u002F\u002Flinen-slack.kedro.org\u002F).\n\nWe keep a list of [technical FAQs in the Kedro documentation](https:\u002F\u002Fdocs.kedro.org\u002Fen\u002Fstable\u002Fgetting-started\u002Ffaq\u002F) and you can find a growing list of blog posts, videos and projects that use Kedro over on the [`awesome-kedro` GitHub repository](https:\u002F\u002Fgithub.com\u002Fkedro-org\u002Fawesome-kedro). If you have created anything with Kedro we'd love to include it on the list. Just make a PR to add it!\n\n## How can I cite Kedro?\n\nIf you're an academic, Kedro can also help you, for example, as a tool to solve the problem of reproducible research. Use the \"Cite this repository\" button on [our repository](https:\u002F\u002Fgithub.com\u002Fkedro-org\u002Fkedro) to generate a citation from the [CITATION.cff file](https:\u002F\u002Fdocs.github.com\u002Fen\u002Frepositories\u002Fmanaging-your-repositorys-settings-and-features\u002Fcustomizing-your-repository\u002Fabout-citation-files).\n\n## Python version support policy\n* The core [Kedro Framework](https:\u002F\u002Fgithub.com\u002Fkedro-org\u002Fkedro) supports all Python versions that are actively maintained by the CPython core team. When a [Python version reaches end of life](https:\u002F\u002Fdevguide.python.org\u002Fversions\u002F#versions), support for that version is dropped from Kedro. This is not considered a breaking change.\n* The [Kedro Datasets](https:\u002F\u002Fgithub.com\u002Fkedro-org\u002Fkedro-plugins\u002Ftree\u002Fmain\u002Fkedro-datasets) package follows the [NEP 29](https:\u002F\u002Fnumpy.org\u002Fneps\u002Fnep-0029-deprecation_policy.html) Python version support policy. This means that `kedro-datasets` generally drops Python version support before `kedro`. This is because `kedro-datasets` has a lot of dependencies that follow NEP 29 and the more conservative version support approach of the Kedro Framework makes it hard to manage those dependencies properly.\n\n\n## ☕️ Kedro Coffee Chat 🔶\n\nWe appreciate our community and want to stay connected. For that, we offer a public Coffee Chat format where we share updates and cool stuff around Kedro once every two weeks and give you time to ask your questions live.\n\nCheck out the upcoming demo topics and dates at the [Kedro Coffee Chat wiki page](https:\u002F\u002Fgithub.com\u002Fkedro-org\u002Fkedro\u002Fwiki\u002FKedro-Coffee-Chat).\n\nFollow our Slack [announcement channel](https:\u002F\u002Fkedro-org.slack.com\u002Farchives\u002FC03RKAQ0MGQ) to see Kedro Coffee Chat announcements and access demo recordings.\n","Kedro 是一个用于构建生产级数据工程和数据科学流水线的工具箱。它遵循软件工程的最佳实践，帮助用户创建可复现、易维护且模块化的数据处理流程。该项目支持实验跟踪、机器学习工程及MLOps等核心功能，并以Python语言实现。适用于需要高效管理和部署数据科学项目的场景，如企业级数据分析平台或科研项目的数据处理环节。",2,"2026-06-11 03:24:03","top_topic"]