lakeFS Acquires DVC, Uniting Data Version Control Pioneers to Accelerate AI-Ready Data
As data quantities increase and pipelines become more complex, understanding where data originated, how it changed, and how it is used becomes increasingly important. This is why modern data systems generate more than just data – they also produce data denoting context: metadata. If teams don’t track metadata, they risk losing context that makes their […]
Based on my presentation at PyData Global 2025 When we – engineers – hear the word “compliance,” we tend to roll our eyes. We want to build features, not fill out forms. But here’s good news: the exact same tools that help you debug your code can also keep you out of trouble. In this
Over the last few years, AI and machine learning have moved from research projects into the core of the medical industry. Models now influence diagnosis, treatment planning, surgical systems, and patient outcomes. That shift changes the cost of uncertainty. This year, regulators won’t just evaluate what an AI or ML model does. They will increasingly
Preparing data for AI projects is about more than fast storage or shiny new table formats – it all starts with selecting the right data catalog to anchor your entire ecosystem. The catalog you pick specifies how your tables are discovered, versioned, secured, and evolved, which, in turn, impacts the reliability and clarity of every
2025 was a defining year for lakeFS. Across open source and Enterprise editions, we shipped major capabilities that expanded lakeFS from a powerful data versioning layer into a control plane for AI-Ready Data – spanning structured and unstructured data, multiple public and private clouds, and a growing ecosystem of analytics and ML engines. Here’s our
Sooner or later, every data team will reach a point where things stop working – whether it’s due to team growth, changing business requirements, or advancing pipeline complexity. When facing these issues, leaders start considering a different approach that perfectly balances centralized and decentralized organizational models. A Data Center of Excellence (DCoE) is a centralized
We all love data lakes. They’re just perfect for storing massive volumes of structured, semi-structured, and unstructured data in native file formats. And they let us explore, refine, and analyze petabytes of data constantly pouring in from various sources. But there’s a caveat. The individual files in a data lake lack the necessary information for
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.
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
Apache Iceberg includes built-in table versioning to ensure that all changes to your data are logged, consistent, and recoverable. Instead of overwriting files or relying on task time, Iceberg saves each update as an immutable snapshot, ensuring that readers always see a consistent picture of the table, even during heavy writes. This boosts reliability by
It pays for organizations to treat their data like a product that works like a driving force behind innovation, efficiency, and competitiveness. While data quality is an important aspect, let’s not forget that companies operate in a rapidly changing environment – and their data needs to reflect this by quickly adapting. This is where data
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