What if you could manage your data lake just like you manage code? With rollback, versioning, and branching capabilities on top of your existing data lake?
lakeFS is an open-source project that provides a Git-like version control interface for data lakes, with seamless integration to most data tools and frameworks. lakeFS enables you to easily implement parallel pipelines for experimentation, reproducibility, and CI/CD for data.
And now you can start playing around with lakeFS in a fully functional lakeFS environment, with your own data and all the tools that you are already using. Get your isolated environment, integrate it with the tools you use, and see how it works in an environment similar to your own.
Please note: playground environments will be deleted after one week.
See these features in action:
- Full reproducibility of data and code
- Git-like operations: branch, commit, merge and revert
- Instant reversion of changes to data
- Petabytes scale version control
- Zero-copy branching for frictionless experimentation
- Seamless integration with most data tools and frameworks (Spark, Hive, AWS Athena, Presto)
- Support for AWS S3, Azure Blob Storage, and Google Cloud Storage (GCS)
Start playing here (no email or any additional information required)
One you have experienced the value in lakeFS, go ahead and install lakeFS open source, or sign up for lakeFS Cloud Beta. And don’t forget to share your feedback with us via our community on Slack or GitHub – this is the best way to let us know if any feature you need is missing!Read Related Articles.


Closing the Gap: Lifecycle Management for Data Products
As data practitioners, we use many different terms to talk about what we do – we call it business intelligence, analytics, data pipelines, or insights.


Introducing the Boto S3 Router Package on PyPI!
Introduction It may seem strange at first, but increasingly we cannot be sure when putting or getting data from an object store that the data


Towards Effective DataOps
Gain the confidence to mess with your datawithout making a mess of your data. “If it hurts, do it more often.” is a wise piece