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.
OPEN SOURCE
Pip install dlt and go
With over 1M downloads per month, dlt 1.4 is the most popular production-ready Python library for moving data. You can add dlt to your Python scripts to load 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 a Python file or a Jupyter Notebook cell. You can load data from any source that produces Python data structures, including APIs, files, databases, and more.
WITH DLT+ YOU CAN UPGRADE YOUR DLT PIPELINES
Pip Installable Data Access and Sharing Ensuring Data Compliance and Quality
Does your data platform team consider data architectures that include DuckDB, a data lake and open table formats such as Iceberg?
dlt+ is dlt for data platform teams. It is a toolkit that allows engineers to upgrade dlt pipelines into portable data products. These provide data scientists with much faster access to data in their organization, and the ability to share it back, all while maintaining data compliance and data quality.
dlt+ works where you work - whether that’s local storage, data lakes, open storage formats, Pythonic engines & AI supported workflows.
Want to join our early pilot customers using dlt+ ?
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.
- Julien Chaumond
- CTO/Co-Founder at Hugging Face
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.
- Maximilian Eber
- CPTO & Co-Founder at Taktile
OPEN SOURCE
Access any data you want in Python
Today it is easier to pip install dlt and write a custom pipeline than to setup and configure a traditional ETL platform. In June '24 we crossed 5,000 dlt total custom sources created by the community since we launched dlt in summer '23. Because dlt is code we continue to automate engineering work and pass on productivity gains to organisations using dlt. Our new REST API Source toolkit is a short, declarative configuration driven way of creating sources. dlt-init-openapi is a a new tool that generates pipelines code out of any OpenAPI spec.
dlt has enabled me to completely rewrite all of our core SaaS service pipelines in 2 weeks and have data pipelines in production with full confidence. We also achieved data democracy for our data platform. Our product, business, and operation teams can independently satisfy a majority of their data needs through no-code self-service. The teams built multi-touch attribution for how Harness acquires customers, and models for how Harness customers utilize licenses. If the teams want to build anything else to push the company forward, they don't need to wait for permission or data access to do it.
- Alex Butler
- Senior Data Engineer at Harness