Description

<h1 align="center"> <strong>data load tool (dlt) — the open-source Python library that automates all your tedious data loading tasks</strong> </h1> <p align="center"> Be it a Google Colab notebook, AWS Lambda function, an Airflow DAG, your local laptop,<br/>or a GPT-4 assisted development playground—<strong>dlt</strong> can be dropped in anywhere. </p> <h3 align="center"> 🚀 Join our thriving community of likeminded developers and build the future together! </h3> <div align="center"> <a target="_blank" href="https://dlthub.com/community" style="background:none"> <img src="https://img.shields.io/badge/slack-join-dlt.svg?labelColor=191937&color=6F6FF7&logo=slack" style="width: 260px;" /> </a> </div> <div align="center"> <a target="_blank" href="https://pypi.org/project/dlt/" style="background:none"> <img src="https://img.shields.io/pypi/v/dlt?labelColor=191937&color=6F6FF7"> </a> <a target="_blank" href="https://pypi.org/project/dlt/" style="background:none"> <img src="https://img.shields.io/pypi/pyversions/dlt?labelColor=191937&color=6F6FF7"> </a> <a target="_blank" href="https://pypi.org/project/dlt/" style="background:none"> <img src="https://img.shields.io/pypi/dm/dlt?labelColor=191937&color=6F6FF7"> </a> </div> ## Installation dlt 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. ```sh pip install dlt ``` ## Quick Start Load chess game data from chess.com API and save it in DuckDB: ```python import dlt from dlt.sources.helpers import requests # Create a dlt pipeline that will load # chess player data to the DuckDB destination pipeline = dlt.pipeline( pipeline_name='chess_pipeline', destination='duckdb', dataset_name='player_data' ) # Grab some player data from Chess.com API data = [] for player in ['magnuscarlsen', 'rpragchess']: response = requests.get(f'https://api.chess.com/pub/player/{player}') response.raise_for_status() data.append(response.json()) # Extract, normalize, and load the data pipeline.run(data, table_name='player') ``` Try it out in our **[Colab Demo](https://colab.research.google.com/drive/1NfSB1DpwbbHX9_t5vlalBTf13utwpMGx?usp=sharing)** or directly on our wasm-based [playground](https://dlthub.com/docs/tutorial/playground) in our docs. ## Features dlt 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://dlthub.com/docs/dlt-ecosystem/llm-tooling/llm-native-workflow) will take your pipeline code to data in a notebook for over [5000 sources](https://dlthub.com/workspace). dlt is designed to be easy to use, flexible, and scalable: - dlt extracts data from [REST APIs](https://dlthub.com/docs/tutorial/rest-api), [SQL databases](https://dlthub.com/docs/tutorial/sql-database), [cloud storage](https://dlthub.com/docs/tutorial/filesystem), [Python data structures](https://dlthub.com/docs/tutorial/load-data-from-an-api), and [many more](https://dlthub.com/docs/dlt-ecosystem/verified-sources). - dlt infers [schemas](https://dlthub.com/docs/general-usage/schema) and [data types](https://dlthub.com/docs/general-usage/schema/#data-types), [normalizes the data](https://dlthub.com/docs/general-usage/schema/#data-normalizer), and handles nested data structures. - dlt supports a variety of [popular destinations](https://dlthub.com/docs/dlt-ecosystem/destinations/) and has an interface to add [custom destinations](https://dlthub.com/docs/dlt-ecosystem/destinations/destination) to create reverse ETL pipelines. - dlt automates pipeline maintenance with [incremental loading](https://dlthub.com/docs/general-usage/incremental-loading), [schema evolution](https://dlthub.com/docs/general-usage/schema-evolution), and [schema and data contracts](https://dlthub.com/docs/general-usage/schema-contracts). - dlt supports [Python and SQL data access](https://dlthub.com/docs/general-usage/dataset-access/), [transformations](https://dlthub.com/docs/dlt-ecosystem/transformations), [pipeline inspection](https://dlthub.com/docs/general-usage/dashboard.md), and [visualizing data in Marimo Notebooks](https://dlthub.com/docs/general-usage/dataset-access/marimo). - dlt can be deployed anywhere Python runs, be it on [Airflow](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer), [serverless functions](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline/deploy-with-google-cloud-functions), or any other cloud deployment of your choice. ## Documentation For detailed usage and configuration, please refer to the [official documentation](https://dlthub.com/docs). ## Examples You can find examples for various use cases in the [examples](docs/examples)