lakeFS Acquires DVC, Uniting Data Version Control Pioneers to Accelerate AI-Ready Data
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 If data is messy, incomplete, or outdated, teams are basically flying blind.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
When AWS Goes Down, Your Data Shouldn’t On October 20th, 2025, AWS experienced a significant outage centered in the us-east-1 region. What started as a DNS resolution issue affecting DynamoDB quickly cascaded into widespread failures across major services and applications. From gaming platforms like Fortnite and social apps like Snapchat to enterprise systems and IoT
Organizations looking to unlock the value from their data are bound to encounter the challenge of dealing with diverse datasets. This includes data in various formats, sources, structures, and semantics, such as structured databases and spreadsheets, as well as unstructured text, photos, and sensor outputs. Digital ecosystems will only become more complex, so the ability
Ask any data team, and you’ll quickly learn that nobody out there manages all the organization’s data in a single centralized location. Most teams operate across various clouds, locations, and platforms, facing increasingly fragmented, replicated, and decentralized data. This makes effective distributed data management an essential capability. Keep reading this article to explore the fundamental
A solid AI data infrastructure is a key enabler for teams looking to efficiently deploy ML applications. It delivers the fundamental features required to enable the whole machine learning lifecycle, from data import and model training to deployment, monitoring, and scaling. Without infrastructure, you’re bound to face obstacles in performance, cooperation, and dependability. This article
One of the major pain points in ML is the lack of transparency, consistency, and control over data assets. Without a centralized system, teams often struggle with fragmented datasets, unclear version histories, and poor documentation, which may lead to reproducibility failures, compliance risks, and wasted effort. A data registry solves this by offering a structured
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