lakeFS Acquires DVC, Uniting Data Version Control Pioneers to Accelerate AI-Ready Data
TL;DR CytoReason is a technology company transforming biopharma’s decision-making—from trial and error to data-driven—through its AI platform of computational disease models. Leveraging an extensive database of public and proprietary data, the company maps human diseases tissue by tissue and cell by cell. Researchers at leading pharma companies, including Pfizer and Sanofi, rely on CytoReason’s technology […]
Data practitioners rarely need convincing to prioritize data quality – it’s already a top-of-mind concern for most. If data is messy, incomplete, or outdated, teams are basically flying blind. Gartner estimates that poor data quality costs businesses around $12.9 million a year. But ask anyone about metadata quality and you might get a blank stare.
This post recaps a comprehensive tutorial published by Alex Merced from Dremio and Tal Sofer from lakeFS, highlighting how version control transforms multimodal data management for AI teams. The Challenge: Keeping Diverse Data Types in Sync and Queriable Modern AI pipelines consume more than just structured data. Training sets include images, model artifacts, logs, and
Multimodal data is nothing else than data gathered from several sources or formats, such as text, photos, audio, video, and sensor readings. What value does it bring to teams? It collectively provides a more complete, holistic view of the environment. This is especially relevant to AI systems learning to understand and interact with humans more
Making Sense of Large-Scale Data Through Metadata Picture this: Your ML team needs to find all images labeled “defective” from Q3 production runs tagged by a specific annotation workflow to retrain a quality control model. In a data lake with 10 billion objects, how do you find them? For most teams, the answer is: you
From Inspiration to Action When we were still dreaming up lakeFS, one of the projects that inspired us was DVC (Data Version Control). It was one of those moments when you realize – “Ah, others see it too.” We weren’t alone in believing that data should be managed like code. DVC was built by data
Many lakeFS customers in the aerospace, automotive, healthcare & life sciences, and manufacturing industries also are heavy users of MATLAB. lakeFS solves a range of data ops challenges for these organizations by serving as a “control plane” for AI-ready data – versioning complex data pipelines, tracking metadata and lineage, and enabling team collaboration through git-like
We’re excited to share that lakeFS has been named a Representative Vendor in the 2025 Gartner® Market Guide for DataOps Tools. We believe this recognition reflects what we’re seeing across the industry: the urgent need for data infrastructures that can provide AI-ready data efficiently, repeatably, and safely as organizations build production AI systems. DataOps Market
Data compliance is all about adhering to laws, regulations, standards, and internal policies regarding data use. Organizations must comply with regulations like the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), the California Consumer Privacy Act (CCPA) and SOC2 standards to protect sensitive information and maintain trust. Data compliance plays
lakeFS Enterprise offers a fully standards-compliant implementation of the Apache Iceberg REST Catalog, enabling Git-style version control for structured data at scale. This integration allows teams to use Iceberg-compatible tools like Spark, Trino, and PyIceberg without any vendor lock-in or proprietary formats. By treating Iceberg tables as versioned entities within lakeFS repositories and branches, users
A behind-the-scenes look at the design decisions, architecture, and lessons learned while bringing the Apache Iceberg REST Catalog to lakeFS. When we first announced our native lakeFS Iceberg REST Catalog, we focused on what it means for data teams: seamless, Git-like version control for structured and unstructured data, at any scale. But how did we
While AI’s promise of increased efficiency and innovation is clear, executing on it is proving to be a major challenge, especially at scale. At lakeFS, we’ve been at the center of this challenge since the beginning. And today, we are excited to announce a $20 million growth funding round, which helps us to double down
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