lakeFS Acquires DVC, Uniting Data Version Control Pioneers to Accelerate AI-Ready Data
We are excited to share that lakeFS is named in the “Coolest Vendor Innovations in Data Management” report from Gartner, published on July 29. The report states “In 2026, there is tremendous interest in deploying AI agents to automate data engineering tasks and operational and analytical workflows. Both vendors and end-user companies are investing heavily […]
As organizations incorporate multimodal data like images, video, music, documents, sensor data, and embeddings into increasingly complex AI systems, traditional data management techniques quickly become insufficient. Suddenly, teams are dealing with different data formats, interconnected pipelines, rising storage costs, and the issue of ensuring consistency and reproducibility. This article guides you through the fundamental ideas
A Frozen Model Is Not a Frozen System Talk to enough robotics teams and you hear the same line: “We’re using off-the-shelf models, so tracking what trained them isn’t really our problem.” It sounds reasonable. Someone else pre-trained the backbone. Your team didn’t touch the weights. Why would you need to reproduce training data you
Branching your database is the start. Branch your whole data ecosystem. Databricks published a good post recently about Lakebase, walking through how Glaspoort, a fiber operator in the Netherlands, ships database changes with the same discipline they ship application code. Every environment branches from production. Every pull request gets its own fresh, disposable database. The
Building AI that works once is relatively easy. Building AI you can trust every time is a discipline. That’s the central lesson CNH – one of the leading manufacturers of agricultural and construction equipment – learned as its machine learning team grew into an engineering organization operating at the sharp edge of safety-critical systems. The
Data has long been described as the foundation of AI. That part is not new. What is changing is the role data plays. In a recent presentation at the AI-Ready Data Summit, Himabindu Tummala, Distinguished Engineer at Dell Technologies, showed how AI is evolving right now and what that evolution demands from the data layer.
Traditional data architectures are starting to show their limitations as organizations move beyond analytics and into production with generative AI and autonomous apps. Fragmented data, pipelines riddled with issues, and ineffective governance make it impossible for teams to replicate or trust AI outputs. Organizations need to provide data teams with a solid architecture to produce
In 1869, chemistry was a growing field, with a growing problem: 63 known elements and no organizing system that worked. Dmitri Mendeleev solved it by inventing the periodic table, which brought order to a field that had outgrown anyone’s ability to track it. Agentic AI is at its 1869 moment, though our challenge is even
When agents become the primary consumers of data, organizations need a secure, reproducible, and governed way to manage how those agents reach it. This article covers how AI agents access enterprise data in practice: the four access models, the core components behind them, the risks that show up in production, and why reproducibility decides whether
Addressing Compliance Requirements at Entrust Why Entrust Moved to lakeFS Tracking PII Access and Storage How lakeFS Improved Validation at Commit Time Indexing and Metadata After Merge The PII Lifecycle Inside the System Soft Deletion on Protected Branches Hard Deletion with Garbage Collection Unexpected Benefits: Versioning and Stable Training Inputs Using Tags and Diffs for
The now-infamous “pizza with glue” AI result is a symptom of something deeper than one bizarre edge case. When AI systems fail, the root cause is rarely mysterious. More often than not, bad outputs can be traced back to bad inputs: flawed data, unclear lineage, or uncontrolled environments. Smarter models won’t fix this on their
AI raises a question most organizations haven’t answered yet: who really controls the foundation? In a recent presentation at the AI-Ready Data Summit, Matthew Miller, Sr. Principal Chief Architect, Field CTO Office at Red Hat, showed that AI sovereignty isn’t a policy debate but an infrastructure strategy. Every AI system depends on choices about data,
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