[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-70731":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":23,"hasPages":23,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":40,"readmeContent":41,"aiSummary":42,"trendingCount":16,"starSnapshotCount":16,"syncStatus":43,"lastSyncTime":44,"discoverSource":45},70731,"dagster","dagster-io\u002Fdagster","dagster-io","An orchestration platform for the development, production, and observation of data assets.","https:\u002F\u002Fdagster.io",null,"Python",15663,2158,155,2182,0,24,63,179,72,45,"Apache License 2.0",false,"master",[26,5,27,28,29,30,31,32,33,34,35,36,37,38,39],"analytics","data-engineering","data-integration","data-orchestrator","data-pipelines","data-science","etl","metadata","mlops","orchestration","python","scheduler","workflow","workflow-automation","2026-06-12 02:02:42","\u003Cdiv align=\"center\">\n  \u003C!-- Note: Do not try adding the dark mode version here with the `picture` element, it will break formatting in PyPI -->\n  \u003Ca target=\"_blank\" href=\"https:\u002F\u002Fdagster.io\" style=\"background:none\">\n    \u003Cimg alt=\"dagster logo\" src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fdagster-io\u002Fdagster\u002Fmaster\u002F.github\u002Fdagster-readme-header.svg\" width=\"auto\" height=\"100%\">\n  \u003C\u002Fa>\n  \u003Ca target=\"_blank\" href=\"https:\u002F\u002Fgithub.com\u002Fdagster-io\u002Fdagster\" style=\"background:none\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Fdagster-io\u002Fdagster?labelColor=4F43DD&color=163B36&logo=github\">\n  \u003C\u002Fa>\n  \u003Ca target=\"_blank\" href=\"https:\u002F\u002Fgithub.com\u002Fdagster-io\u002Fdagster\u002Fblob\u002Fmaster\u002FLICENSE\" style=\"background:none\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-Apache_2.0-blue.svg?label=license&labelColor=4F43DD&color=163B36\">\n  \u003C\u002Fa>\n  \u003Ca target=\"_blank\" href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fdagster\u002F\" style=\"background:none\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fdagster?labelColor=4F43DD&color=163B36\">\n  \u003C\u002Fa>\n  \u003Ca target=\"_blank\" href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fdagster\u002F\" style=\"background:none\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fpyversions\u002Fdagster?labelColor=4F43DD&color=163B36\">\n  \u003C\u002Fa>\n  \u003Ca target=\"_blank\" href=\"https:\u002F\u002Ftwitter.com\u002Fdagster\" style=\"background:none\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Ftwitter-dagster-blue.svg?labelColor=4F43DD&color=163B36&logo=twitter\" \u002F>\n  \u003C\u002Fa>\n  \u003Ca target=\"_blank\" href=\"https:\u002F\u002Fdagster.io\u002Fslack\" style=\"background:none\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fslack-dagster-blue.svg?labelColor=4F43DD&color=163B36&logo=slack\" \u002F>\n  \u003C\u002Fa>\n  \u003Ca target=\"_blank\" href=\"https:\u002F\u002Flinkedin.com\u002Fshowcase\u002Fdagster\" style=\"background:none\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flinkedin-dagster-blue.svg?labelColor=4F43DD&color=163B36&logo=linkedin\" \u002F>\n  \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n**Dagster is a cloud-native data pipeline orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability.**\n\nIt is designed for **developing and maintaining data assets**, such as tables, data sets, machine learning models, and reports.\n\nWith Dagster, you declare—as Python functions—the data assets that you want to build. Dagster then helps you run your functions at the right time and keep your assets up-to-date.\n\nHere is an example of a graph of three assets defined in Python:\n\n```python\nimport dagster as dg\nimport pandas as pd\n\nfrom sklearn.linear_model import LinearRegression\n\n@dg.asset\ndef country_populations() -> pd.DataFrame:\n    df = pd.read_html(\"https:\u002F\u002Ftinyurl.com\u002Fmry64ebh\")[0]\n    df.columns = [\"country\", \"pop2022\", \"pop2023\", \"change\", \"continent\", \"region\"]\n    df[\"change\"] = df[\"change\"].str.rstrip(\"%\").astype(\"float\")\n    return df\n\n@dg.asset\ndef continent_change_model(country_populations: pd.DataFrame) -> LinearRegression:\n    data = country_populations.dropna(subset=[\"change\"])\n    return LinearRegression().fit(pd.get_dummies(data[[\"continent\"]]), data[\"change\"])\n\n@dg.asset\ndef continent_stats(country_populations: pd.DataFrame, continent_change_model: LinearRegression) -> pd.DataFrame:\n    result = country_populations.groupby(\"continent\").sum()\n    result[\"pop_change_factor\"] = continent_change_model.coef_\n    return result\n```\n\nThe graph loaded into Dagster's web UI:\n\n\u003Cp align=\"center\">\n  \u003Cimg width=\"100%\" alt=\"An example asset graph as rendered in the Dagster UI\" src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fdagster-io\u002Fdagster\u002Fmaster\u002F.github\u002Fexample-lineage.png\">\n\u003C\u002Fp>\n\nDagster is built to be used at every stage of the data development lifecycle - local development, unit tests, integration tests, staging environments, all the way up to production.\n\n## Quick Start:\n\nIf you're new to Dagster, we recommend checking out the [docs](https:\u002F\u002Fdocs.dagster.io) or following the hands-on [tutorial](https:\u002F\u002Fdocs.dagster.io\u002Fetl-pipeline-tutorial\u002F).\n\nDagster is available on PyPI and officially supports Python 3.9 through Python 3.14.\n\n```bash\nuv add dagster dagster-webserver dagster-dg-cli\n```\n\n## Documentation\n\nYou can find the full Dagster documentation [here](https:\u002F\u002Fdocs.dagster.io), including the [Quickstart guide](https:\u002F\u002Fdocs.dagster.io\u002Fgetting-started\u002Fquickstart).\n\n\u003Chr\u002F>\n\n## Key Features:\n\n  \u003Cp align=\"center\">\n    \u003Cimg width=\"100%\" alt=\"image\" src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fdagster-io\u002Fdagster\u002Fmaster\u002F.github\u002Fkey-features-cards.svg\">\n  \u003C\u002Fp>\n\n### Dagster as a productivity platform\n\nIdentify the key assets you need to create using a declarative approach, or you can focus on running basic tasks. Embrace CI\u002FCD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early.\n\n### Dagster as a robust orchestration engine\n\nPut your pipelines into production with a robust multi-tenant, multi-tool engine that scales technically and organizationally.\n\n### Dagster as a unified control plane\n\nMaintain control over your data as the complexity scales. Centralize your metadata in one tool with built-in observability, diagnostics, cataloging, and lineage. Spot any issues and identify performance improvement opportunities.\n\n\u003Chr \u002F>\n\n## Master the Modern Data Stack with integrations\n\nDagster provides a growing library of integrations for today’s most popular data tools. Integrate with the tools you already use, and deploy to your infrastructure.\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n    \u003Ca target=\"_blank\" href=\"https:\u002F\u002Fdagster.io\u002Fintegrations\" style=\"background:none\">\n        \u003Cimg width=\"100%\" alt=\"image\" src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fdagster-io\u002Fdagster\u002Fmaster\u002F.github\u002Fintegrations-bar-for-readme.png\">\n    \u003C\u002Fa>\n\u003C\u002Fp>\n\n## Community\n\nConnect with thousands of other data practitioners building with Dagster. Share knowledge, get help,\nand contribute to the open-source project. To see featured material and upcoming events, check out\nour [Dagster Community](https:\u002F\u002Fdagster.io\u002Fcommunity) page.\n\nJoin our community here:\n\n- 🌟 [Star us on GitHub](https:\u002F\u002Fgithub.com\u002Fdagster-io\u002Fdagster)\n- 📥 [Subscribe to our Newsletter](https:\u002F\u002Fdagster.io\u002Fnewsletter-signup)\n- 🐦 [Follow us on Twitter](https:\u002F\u002Ftwitter.com\u002Fdagster)\n- 🕴️ [Follow us on LinkedIn](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fdagsterlabs\u002F)\n- 📺 [Subscribe to our YouTube channel](https:\u002F\u002Fwww.youtube.com\u002F@dagsterio)\n- 📚 [Read our blog posts](https:\u002F\u002Fdagster.io\u002Fblog)\n- 👋 [Join us on Slack](https:\u002F\u002Fdagster.io\u002Fslack)\n- 🗃 [Browse Slack archives](https:\u002F\u002Fdiscuss.dagster.io)\n- ✏️ [Start a GitHub Discussion](https:\u002F\u002Fgithub.com\u002Fdagster-io\u002Fdagster\u002Fdiscussions)\n\n## Contributing\n\nFor details on contributing or running the project for development, check out our [contributing\nguide](https:\u002F\u002Fdocs.dagster.io\u002Fabout\u002Fcontributing).\n\n## License\n\nDagster is [Apache 2.0 licensed](https:\u002F\u002Fgithub.com\u002Fdagster-io\u002Fdagster\u002Fblob\u002Fmaster\u002FLICENSE).\n","Dagster 是一个用于开发、生产和监控数据资产的编排平台。它支持声明式的编程模型，允许用户通过 Python 函数定义数据资产，并自动管理这些资产的更新和执行流程。Dagster 提供了集成的数据血缘追踪和可观测性功能，确保数据处理过程的透明性和可调试性。此外，该工具还具备一流的测试能力，有助于提高数据工程项目的可靠性和维护效率。适用于需要构建复杂数据流水线（如 ETL 作业、机器学习模型训练等）以及追求高效运维的数据科学团队或企业。",2,"2026-06-11 03:33:54","high_star"]