Data engineering best practices
using git-like operations on data

The cloud never warned us about the data getting clouded. As the blessing of infinite storage quickly became an unmanageable mess, there is a need for technologies like lakeFS to make data accessible again

Sivan Bercovici,
CTO

Since introducing lakeFS to our production data environment, we’ve enjoyed the benefits of atomic and isolated operations in our data pipelines. This has allowed us to spend more time improving other aspects of our data platform, and less time dealing with the fallout from race conditions and partially failed operations

Lior Resisi,
Data Platform Team Lead

By using lakeFS we produce a commit history on the production branch that easily allows for rollbacks. In the case of data quality issues in production, this allows us to simply revert to the previous high quality snapshot of our data.

Doron Grinzaig
Big Data R&D Team Lead

With lakeFS we can easily achieve advanced use cases with data, such as running parallel pipelines with different logic to experiment or conduct what-if analysis, compare large result sets for data science and machine learning, and more

Stephen Seewald,
Raghvendra Verma,
Cory Matheson

Moving to a data branching solution has paid off quickly for us. A few days after completing the migration, we’ve already reduced testing time by 80% on two different projects. And we’re excited to see how data branching increases our product velocity.

Ryan Green,
CTO