[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-71170":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":37,"readmeContent":38,"aiSummary":39,"trendingCount":16,"starSnapshotCount":16,"syncStatus":40,"lastSyncTime":41,"discoverSource":42},71170,"dlt","dlt-hub\u002Fdlt","dlt-hub","data load tool (dlt) is an open source Python library that makes data loading easy 🛠️ ","https:\u002F\u002Fdlthub.com\u002Fdocs",null,"Python",5449,525,26,292,0,9,37,142,27,110.66,"Apache License 2.0",false,"devel",true,[27,28,29,30,31,32,33,34,35,36],"data","data-engineering","data-lake","data-loading","data-warehouse","elt","extract","load","python","transform","2026-06-12 04:00:59","\u003Ch1 align=\"center\">\n    \u003Cstrong>data load tool (dlt) — the open-source Python library that automates all your tedious data loading tasks\u003C\u002Fstrong>\n\u003C\u002Fh1>\n\u003Cp align=\"center\">\nBe it a Google Colab notebook, AWS Lambda function, an Airflow DAG, your local laptop,\u003Cbr\u002F>or a GPT-4 assisted development playground—\u003Cstrong>dlt\u003C\u002Fstrong> can be dropped in anywhere.\n\u003C\u002Fp>\n\n\n\u003Ch3 align=\"center\">\n\n🚀 Join our thriving community of likeminded developers and build the future together!\n\n\u003C\u002Fh3>\n\n\u003Cdiv align=\"center\">\n  \u003Ca target=\"_blank\" href=\"https:\u002F\u002Fdlthub.com\u002Fcommunity\" style=\"background:none\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fslack-join-dlt.svg?labelColor=191937&color=6F6FF7&logo=slack\" style=\"width: 260px;\"  \u002F>\n  \u003C\u002Fa>\n\u003C\u002Fdiv>\n\u003Cdiv align=\"center\">\n  \u003Ca target=\"_blank\" href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fdlt\u002F\" style=\"background:none\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fdlt?labelColor=191937&color=6F6FF7\">\n  \u003C\u002Fa>\n  \u003Ca target=\"_blank\" href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fdlt\u002F\" style=\"background:none\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fpyversions\u002Fdlt?labelColor=191937&color=6F6FF7\">\n  \u003C\u002Fa>\n  \u003Ca target=\"_blank\" href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fdlt\u002F\" style=\"background:none\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fdm\u002Fdlt?labelColor=191937&color=6F6FF7\">\n  \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n## Installation\n\ndlt supports Python 3.9 through Python 3.14. Note that some optional extras are not yet available for Python 3.14, so support for this version is considered experimental.\n\n```sh\npip install dlt\n```\n\n## Quick Start\n\nLoad chess game data from chess.com API and save it in DuckDB:\n\n```python\nimport dlt\nfrom dlt.sources.helpers import requests\n\n# Create a dlt pipeline that will load\n# chess player data to the DuckDB destination\npipeline = dlt.pipeline(\n    pipeline_name='chess_pipeline',\n    destination='duckdb',\n    dataset_name='player_data'\n)\n\n# Grab some player data from Chess.com API\ndata = []\nfor player in ['magnuscarlsen', 'rpragchess']:\n    response = requests.get(f'https:\u002F\u002Fapi.chess.com\u002Fpub\u002Fplayer\u002F{player}')\n    response.raise_for_status()\n    data.append(response.json())\n\n# Extract, normalize, and load the data\npipeline.run(data, table_name='player')\n```\n\n\nTry it out in our **[Colab Demo](https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1NfSB1DpwbbHX9_t5vlalBTf13utwpMGx?usp=sharing)** or directly on our wasm-based [playground](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Ftutorial\u002Fplayground) in our docs.\n\n## Features\n\ndlt is an open-source Python library that loads data from various, often messy data sources into well-structured datasets. It provides lightweight Python interfaces to extract, load, inspect, and transform data. dlt and dlt docs are built from the ground up to be used with LLMs: the [LLM-native workflow](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Fdlt-ecosystem\u002Fllm-tooling\u002Fllm-native-workflow) will take your pipeline code to data in a notebook for over [5000 sources](https:\u002F\u002Fdlthub.com\u002Fworkspace).\n\ndlt is designed to be easy to use, flexible, and scalable:\n\n- dlt extracts data from [REST APIs](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Ftutorial\u002Frest-api), [SQL databases](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Ftutorial\u002Fsql-database), [cloud storage](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Ftutorial\u002Ffilesystem), [Python data structures](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Ftutorial\u002Fload-data-from-an-api), and [many more](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Fdlt-ecosystem\u002Fverified-sources).\n- dlt infers [schemas](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Fgeneral-usage\u002Fschema) and [data types](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Fgeneral-usage\u002Fschema\u002F#data-types), [normalizes the data](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Fgeneral-usage\u002Fschema\u002F#data-normalizer), and handles nested data structures.\n- dlt supports a variety of [popular destinations](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Fdlt-ecosystem\u002Fdestinations\u002F) and has an interface to add [custom destinations](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Fdlt-ecosystem\u002Fdestinations\u002Fdestination) to create reverse ETL pipelines.\n- dlt automates pipeline maintenance with [incremental loading](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Fgeneral-usage\u002Fincremental-loading), [schema evolution](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Fgeneral-usage\u002Fschema-evolution), and [schema and data contracts](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Fgeneral-usage\u002Fschema-contracts).\n- dlt supports [Python and SQL data access](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Fgeneral-usage\u002Fdataset-access\u002F), [transformations](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Fdlt-ecosystem\u002Ftransformations), [pipeline inspection](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Fgeneral-usage\u002Fdashboard.md), and [visualizing data in Marimo Notebooks](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Fgeneral-usage\u002Fdataset-access\u002Fmarimo).\n- dlt can be deployed anywhere Python runs, be it on [Airflow](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Fwalkthroughs\u002Fdeploy-a-pipeline\u002Fdeploy-with-airflow-composer), [serverless functions](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Fwalkthroughs\u002Fdeploy-a-pipeline\u002Fdeploy-with-google-cloud-functions), or any other cloud deployment of your choice.\n\n## Documentation\n\nFor detailed usage and configuration, please refer to the [official documentation](https:\u002F\u002Fdlthub.com\u002Fdocs).\n\n## Examples\n\nYou can find examples for various use cases in the [examples](docs\u002Fexamples) folder, or in the [code examples section](https:\u002F\u002Fdlthub.com\u002Fdocs\u002Fexamples) of our docs page.\n\n## Adding as dependency\n\n`dlt` follows the semantic versioning with the [`MAJOR.MINOR.PATCH`](https:\u002F\u002Fpeps.python.org\u002Fpep-0440\u002F#semantic-versioning) pattern.\n\n* `major` means breaking changes and removed deprecations\n* `minor` new features, sometimes automatic migrations\n* `patch` bug fixes\n\nWe suggest that you allow only `patch` level updates automatically using the [Compatible Release Specifier](https:\u002F\u002Fpackaging.python.org\u002Fen\u002Flatest\u002Fspecifications\u002Fversion-specifiers\u002F#compatible-release). For example **dlt~=1.23.0** allows only versions **>=1.23.0** and less than **\u003C1.24.0**\n\nPlease also see our [release notes](https:\u002F\u002Fgithub.com\u002Fdlt-hub\u002Fdlt\u002Freleases) for notable changes between versions.\n\n## Get Involved\n\nThe dlt project is quickly growing, and we're excited to have you join our community! Here's how you can get involved:\n\n- **Connect with the Community**: Join other dlt users and contributors on our [Slack](https:\u002F\u002Fdlthub.com\u002Fcommunity)\n- **Report issues and suggest features**: Please use the [GitHub Issues](https:\u002F\u002Fgithub.com\u002Fdlt-hub\u002Fdlt\u002Fissues) to report bugs or suggest new features. Before creating a new issue, make sure to search the tracker for possible duplicates and add a comment if you find one.\n- **Track progress of our work and our plans**: Please check out our [public Github project](https:\u002F\u002Fgithub.com\u002Forgs\u002Fdlt-hub\u002Fprojects\u002F9)\n- **Improve documentation**: Help us enhance the dlt documentation.\n\n## Contribute code\nPlease read [CONTRIBUTING](CONTRIBUTING.md) before you make a PR.\n\n- 📣 **New destinations are unlikely to be merged** due to high maintenance cost (but we are happy to improve SQLAlchemy destination to handle more dialects)\n- Significant changes require tests and docs and in many cases writing tests will be more laborious than writing code\n- Bugfixes and improvements are welcome! You'll get help with writing tests and docs + a decent review.\n\n## Sponsors\n\n\u003Cp>\n  \u003Ca href=\"https:\u002F\u002Fblacksmith.sh\u002F?utm_source=dlt&utm_medium=readme&utm_campaign=sponsorship\" target=\"_blank\">\n    \u003Cimg src=\".github\u002Fassets\u002Fblacksmith-logo.svg\" alt=\"Blacksmith\" width=\"240\" \u002F>\n  \u003C\u002Fa>\n\u003C\u002Fp>\n\n[Blacksmith](https:\u002F\u002Fblacksmith.sh\u002F?utm_source=dlt&utm_medium=readme&utm_campaign=sponsorship) is a CI\u002FCD platform that accelerates GitHub Actions by reducing queue times and improving build performance. It helps teams run workflows faster and more efficiently, making it easier to maintain a smooth development pipeline. We're grateful to Blacksmith for sponsoring dlt with free CI\u002FCD minutes, which helps us keep builds fast and our costs lower.\n\n## License\n\n`dlt` is released under the [Apache 2.0 License](LICENSE.txt).\n","dlt是一个开源的Python库，旨在简化数据加载过程。它提供了一套轻量级的Python接口来实现数据抽取、加载、检查和转换，并且能够处理多种复杂的数据源，将数据转化为结构良好的数据集。该工具支持从REST APIs等来源提取数据，并能与Google Colab笔记本、AWS Lambda函数、Airflow DAG等多种环境无缝集成。此外，dlt还特别优化了对大型语言模型（LLM）的支持，使其成为构建高效ETL流程的理想选择。适用于需要快速搭建或扩展数据管道的各种场景，无论是个人开发者还是企业团队都能从中受益。",2,"2026-06-11 03:36:25","high_star"]