If you think about it, lakeFS is about two things — version control and big data. We see ourselves as bringing version control to big data. This bridges a workflow gap that currently exists when working with data and working with code.
This gap is purely artificial — there’s no conceptual reason why different workflows should be required for each. In fact, since they are almost always related in the form of executed code running over bytes of data, in many cases it makes sense to treat them as one entity from a source control perspective.
This is what we are making possible with the lakeFS project — version control at scale. We believe this is a crucial piece of what’s missing from the developer experience of the most performant data platforms.
Processing data at scale isn’t enough anymore, we want to do so with grace.
If I’ve managed to piqued your interest, check out the demo video below, which goes into more detail and shows lakeFS in action!
About lakeFS
The lakeFS project is an open source technology that provides a git-like version control interface for data lakes, with seamless integration to popular data tools and frameworks.
Our mission is to maximize the manageability of open source data analytics solutions that scale.
Read Related Articles.

Data Lake Mount for Efficient Data Sharing & Versioned Lake Management
Mounting object storage as a filesystem is the fastest way to get a notebook or Spark job reading S3, Azure Data Lake Storage, or GCS

Multimodal Data Integration: Architecture, Challenges & Best Practices
As AI systems scale, data bottlenecks for AI projects quickly become one of the key barriers to model development and deployment. Slow pipelines, inconsistent datasets,

Beyond RAG: Put Open Knowledge Format Bundles Into Production with lakeFS
Traditional RAG often feels like a black box. Vector search is powerful, but hard to reason about, and that makes AI agent knowledge versioning nearly



