lakeFS, the pioneering open-source platform bringing Git-style version control to data lakes, has raised $20 million in fresh funding. The round was led by Maor Investments, with participation from Dell Technologies Capital, Norwest, and Zeev Ventures. The funding gives lakeFS the firepower to accelerate its mission: transforming chaotic data pipelines into structured, auditable, and scalable workflows.

Founded by Dr. Einat Orr, a seasoned systems architect and one of the foremost thought leaders in data infrastructure, lakeFS is solving a foundational bottleneck in AI development. While model-building and deployment have seen massive innovation, the data layer - where everything starts - has lagged behind in terms of repeatability, testing, and version safety. lakeFS is changing that by enabling teams to treat data as code.

Empowering Teams to Version, Branch, and Merge Data

lakeFS enables data teams to create isolated branches of datasets, experiment in parallel, and merge updates without jeopardizing production pipelines. These Git-like primitives, familiar to software developers, offer a game-changing experience to data scientists and engineers who have long suffered under brittle, ad-hoc ETL scripts and manually tracked changes.

By making it possible to roll back data pipelines and audit every transformation, lakeFS unlocks a more rigorous, collaborative, and fail-safe approach to managing machine learning workflows, governance, and experimentation at scale.

The ability to version data isn't just a convenience - it's an operational necessity in regulated environments like finance, healthcare, and government. And as AI becomes core infrastructure in these sectors, demand for reliable, testable data workflows has reached a tipping point.

The Insight That Made lakeFS Investable

As lakeFS grew, the team made a pivotal observation: modern enterprises are drowning in tooling but starving for control. Instead of building yet another monitoring dashboard or orchestration layer, lakeFS solved the trust issue at its root - by giving teams the power to experiment with data without fear of breaking production.

And here’s where the real insight comes in - one that more technical founders should internalize. The company didn’t try to “educate the market” on a new way of thinking. It mapped directly to workflows engineers already understood from software development. The secret wasn't to teach Git-like workflows for data; it was to adopt Git’s philosophy and embed it into the reality of data teams - branching, isolation, reproducibility, commit history.

This alignment allowed lakeFS to sidestep the usual resistance to infrastructure change. They weren’t selling disruption; they were offering familiarity in a broken domain. That framing - “this is just like Git, but for data” - gave them an immediate narrative edge with both engineers and investors.

Founders building deep tech should take note: the fastest way to adoption is often through metaphor. If your product introduces a paradigm shift, find a handle people already trust.

Founder-Led Execution and Community Momentum

Dr. Einat Orr has led lakeFS with both technical depth and organizational clarity. Her approach to leadership is distinctly product-led, pushing for clean APIs, open-source contributions, and seamless integrations with existing data stacks like Spark, Hive, Presto, and Airflow.

Rather than prioritizing top-down enterprise sales out of the gate, lakeFS leaned into bottom-up adoption. It seeded usage through open-source and grew from developer advocacy - not just polished decks. This community-first approach enabled them to refine their core product in the wild while learning which features mattered most for enterprise-grade environments.

Today, lakeFS powers production data environments at major global companies, where repeatability, auditing, and rollback aren’t nice-to-haves - they're make-or-break features for compliance and uptime.

Strategic Investors Backing a Foundational Shift

The participation of Dell Technologies Capital, Norwest, and Zeev Ventures in this round sends a clear signal: data version control is no longer a fringe innovation. It’s becoming foundational to any organization that builds, deploys, and scales machine learning applications.

For investors, lakeFS represents an infrastructure play with long-term defensibility. It sits in the control plane - right where tooling meets process - and creates a sticky developer experience that naturally expands inside teams. Once adopted, it’s not just hard to rip out - it becomes a core pillar of trust.

What’s Ahead for lakeFS

The company will use this $20M infusion to accelerate both product development and go-to-market initiatives. That includes deeper integration with modern data stacks, enhanced enterprise features like policy automation and RBAC, and expanding the capabilities of its data catalog for large-scale deployments.

lakeFS is also investing in auto-remediation and intelligent suggestions for pipeline errors, version conflicts, and schema drift - laying the groundwork for what could become a self-correcting data infrastructure.

Their North Star? A world where engineers can experiment fearlessly, data scientists can collaborate without friction, and machine learning teams can operate with the same velocity and safety that software engineers enjoy today.