Treeverse Labs

Company Overview

Snapshot

Founded in January 2020 by Einat Orr and Or Katz, Treeverse Labs operates with 11–50 employees. The company has raised $43 million across three funding rounds from four investors.

Business overview

Treeverse Labs develops lakeFS, an open-source platform designed to bring resilience and manageability to object-storage-based data lakes. Its core technology enables users to build repeatable, atomic, and versioned data lake operations, supporting complex ETL jobs, data science, and analytics. The platform is compatible with Amazon Web Services S3 and Google Cloud Storage and integrates seamlessly with modern data frameworks like Spark, Hive, AWS Athena, and Presto, serving the business software sector across Europe and the United States.

Strategic signal

In February 2022, Treeverse Labs' lakeFS platform expanded its capabilities by introducing branching for data lakes, a significant development for data management. This innovation allows data teams to implement Git-like version control, enabling isolated environments for experimentation, rollbacks, and enhanced reproducibility. This signals a strategic move to provide more robust and flexible data governance solutions, addressing critical needs for data integrity and operational efficiency in enterprise data environments.

Company Intelligence Q&A

What is Treeverse Labs' primary product?

Treeverse Labs' primary product is lakeFS, an open-source platform that provides Git-like version control for data lakes, enhancing their resilience and manageability.

When was Treeverse Labs founded?

Treeverse Labs was founded in January 2020 by Einat Orr and Or Katz.

Which investors participated in Treeverse Labs' July 2025 funding round?

In July 2025, Treeverse Labs received funding led by.

What is the total capital raised by Treeverse Labs?

Treeverse Labs has raised a total of $43 million across three funding rounds.

What is a key use case for lakeFS?

One key use case for lakeFS is enabling isolated environments, allowing data teams to use Git-like branching for safe, independent, and version-controlled experimentation and testing without impacting production data.