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dlt is an open-source python-first scalable data loading library that does not require any backend to run.

Reason this release was yanked:

Data-loss bug: incremental merge truncates the destination table. Fixed in 1.27.2

Project description

data load tool (dlt) — the open-source Python library that automates all your tedious data loading tasks

Be it a Google Colab notebook, AWS Lambda function, an Airflow DAG, your local laptop,
or a GPT-4 assisted development playground—dlt can be dropped in anywhere.

🚀 Join our thriving community of likeminded developers and build the future together!

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.

pip install dlt

Quick Start

Load chess game data from chess.com API and save it in DuckDB:

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 or directly on our wasm-based 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 will take your pipeline code to data in a notebook for over 5000 sources.

dlt is designed to be easy to use, flexible, and scalable:

Documentation

For detailed usage and configuration, please refer to the official documentation.

Examples

You can find examples for various use cases in the examples folder, or in the code examples section of our docs page.

Adding as dependency

dlt follows the semantic versioning with the MAJOR.MINOR.PATCH pattern.

  • major means breaking changes and removed deprecations
  • minor new features, sometimes automatic migrations
  • patch bug fixes

We suggest that you allow only patch level updates automatically using the Compatible Release Specifier. For example dlt~=1.23.0 allows only versions >=1.23.0 and less than <1.24.0

Please also see our release notes for notable changes between versions.

Get Involved

The dlt project is quickly growing, and we're excited to have you join our community! Here's how you can get involved:

  • Connect with the Community: Join other dlt users and contributors on our Slack
  • Report issues and suggest features: Please use the GitHub Issues 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.
  • Track progress of our work and our plans: Please check out our public Github project
  • Improve documentation: Help us enhance the dlt documentation.

Contribute code

Please read CONTRIBUTING before you make a PR.

  • 📣 New destinations are unlikely to be merged due to high maintenance cost (but we are happy to improve SQLAlchemy destination to handle more dialects)
  • Significant changes require tests and docs and in many cases writing tests will be more laborious than writing code
  • Bugfixes and improvements are welcome! You'll get help with writing tests and docs + a decent review.

Sponsors

Blacksmith

Blacksmith is a drop-in replacement for GitHub-hosted runners that speed up our CI/CD pipelines by 2x and up to 75% cheaper. We're grateful to Blacksmith for sponsoring us with free CI/CD minutes--which helps us keep builds fast and our costs lower.

License

dlt is released under the Apache 2.0 License.

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