dltHub

Lightweight Python code to move data

We focus on the needs & constraints of Python-first data platform teams: how to write any data source, achieve data democracy, modernise legacy systems and reduce cloud costs.

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OPEN SOURCE (DLT - DATA LOAD TOOL)

pip install dlt and go

dlt (data load tool) is the most popular production-ready Python library for moving data. It loads data from various and often messy data sources into well-structured, live datasets.

Unlike other non-Python solutions, with dlt, there's no need to use any backends or containers. We do not replace your data platform, deployments, or security models. Simply import dlt in your favorite AI code editor, or add it to your Jupyter Notebook. You can load data from any source that produces Python data structures, including APIs, files, databases, and more.

Get started
import dlt
from dlt.sources.filesystem import filesystem

resource = filesystem(
    bucket_url="s3://example-bucket",
    file_glob="*.csv"
)

pipeline = dlt.pipeline(
    pipeline_name="filesystem_example",
    destination="duckdb",
    dataset_name="filesystem_data",
)

pipeline.run(resource)
DLTHUB WORKSPACE

From any data source to live report, in one flow

Upgrade your workflow with Workspace, a dedicated environment to create, debug, and maintain dlt pipelines in production - all in one streamlined flow, designed for individual developers.

As the first release of Workspace, we've now launched LLM-native pipeline development for over 1,000 REST API data sources.

Pick any source, grab all the context your LLM needs with our easy dlt ai init commands, and go from ingesting data to delivering reports via Notebooks, in under 10 minutes.

Explore Workspace
EARLY ACCESS DLT+

Upgrade your data platform with dlt+

We're turning the most common in-house patterns into reusable, interoperable components, so you can upgrade your data platform piece by piece, without starting from scratch. We're in early access and partnering with teams upgrading to Iceberg - come build with us.

Learn more
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Destination
Quotation mark icon{testimonial.author?.name}

The current machine learning revolution has been enabled by the Cambrian explosion of Python open-source tools that have become so accessible that a wide range of practitioners can use them. As a simple-to-use Python library, dlt is the first tool that this new wave of people can use. By leveraging this library, we can extend the machine learning revolution into enterprise data.

Quotation mark icon
Julien Chaumond
CTO/Co-Founder at Hugging Face
Quotation mark icon{testimonial.author?.name}

Python and machine learning under security constraints are key to our success. We found that our cloud ETL provider could not meet our needs. dlt is a lightweight yet powerful open source tool we can run together with Snowflake. Our event streaming and batch data loading performs at scale and low cost. Now anyone who knows Python can self-serve to fulfil their data needs.

Quotation mark icon
Maximilian Eber
CPTO & Co-Founder at Taktile

The latest from dltHub

Celebrating our 3,000th OSS dlt customer as dlt’s momentum accelerates
Company Update

Celebrating our 3,000th OSS dlt customer as dlt’s momentum accelerates

Learn why
dlt development roadmap for 2025: a simpler solution for solving complex problems
Product Release

dlt development roadmap for 2025: a simpler solution for solving complex problems

Learn what's next for dlt in 2025
Vibe Coding: Why Building Data Pipelines with dlt & LLMs Actually Works
How-To

Vibe Coding: Why Building Data Pipelines with dlt & LLMs Actually Works

Try it yourself

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