lakeFS Acquires DVC, Uniting Data Version Control Pioneers to Accelerate AI-Ready Data
In this post, we explore how lakeFS can integrate with popular data labeling solutions, the differences between labeling tools’ built-in dataset management and lakeFS data version control, and why combining them is invaluable. We’ll also highlight use cases – from autonomous vehicles to healthcare – where rigorous data versioning alongside labeling is essential. Overview of […]
Historically, companies have developed their IT systems on an ad hoc basis, installing various software and taking on data management approaches as their needs changed. The resulting organization is diverse, with multiple tools and data that serve the same function. Data tends to be segregated and dispersed across teams and areas, with little to no
Vector databases are a critical enabler for expanding the use of LLMs. They power applications such as Retrieval Augmented Generation (RAG), pattern matching, anomaly detection, and recommendation systems by retrieving relevant data for your application. A vector database needs to carry out efficient similarity searches across vector embeddings of both unstructured and structured data. You
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
Organizations deal with ever-increasing volumes of data. More data translates into more risk, as hackers have a larger target area. This is where data compliance comes in. It helps mitigate these threats and protect consumer data by setting compliance standards that companies and individuals must adhere to while working with data. How does data compliance
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
Data integration is a vital first step in developing any AI application. This is where data virtualization comes in to help organizations accelerate application development and deployment. By virtualizing data, teams can unlock its full potential by providing real-time AI insights for applications like predictive maintenance, fraud detection, and demand forecasting. Virtualizing data centralizes and
Modern machine learning pipelines involve a mix of tools for experiment tracking, data preparation, model registry, and more. MLflow, DataChain, Neptune, and Quilt are some MLOps tools serving these needs. However, one critical piece underpins them all: data version control. This is where lakeFS comes in. lakeFS is not an experiment tracker or ML platform;
Most organizations collect massive amounts of data from various sources, including customer interactions, supply networks, financial systems, and more. As a result, teams may feel overwhelmed by a flood of data while seeking key insights, and the question of data manageability becomes more pressing than ever. This is where data discovery comes in. Data discovery
Since 2021, we’ve published the annual State of Data Engineering Report, which includes a summary of all key categories that directly impact data engineering infrastructure. In 2025, we see five primary trends that influence the categories that will be covered in this report. Trend #1: MLOps space is slowly diminishing The MLOps space is slowly
During the 2025 Nvidia GTC conference, one of the keywords that drew a lot of attention was “AI factory.” An AI factory is Nvidia’s idea for producing large-scale AI systems. This concept aligns AI development with the industrial process, in which raw data is received, improved through computation, and converted into valuable products via data-driven
A solid AI infrastructure is essential for efficiently developing and deploying AI and machine learning (ML) applications – from facial and speech recognition to text processing and computer vision. Before we dive into why AI infrastructure is crucial and how it works, let’s define it first. What is AI infrastructure? AI infrastructure, also known as
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