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 […] The post What is Metadata Filtering? Benefits, Best Practices & Tools appeared first on Git for ...| Git for Data – lakeFS
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 […] The post How lakeFS Helps Ensure Data Compliance appeared first on Git for Data -...| Git for Data – lakeFS
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 […] The post Versioned Data with Apache Iceberg Using lakeFS Iceberg REST Catalog ap...| Git for Data – lakeFS
Learn what data compliance is, its benefits, essential tools, and key metrics to protect sensitive information and meet regulations.| Git for Data - lakeFS
Yesterday, OpenAI launched gpt-oss-120b and gpt-oss-20b, marking the company’s first open-weight models since GPT-2 in 2019. This strategic shift represents far more than a product release—it signals a fundamental transformation in how large organizations, particularly in regulated industries, approach AI infrastructure and data management. OpenAI’s Strategic Return to Open Source The gpt-oss models—gpt-oss-120b and gpt-oss-20b—are […] The post OpenAI’s Open Source Revolution: W...| Git for Data – lakeFS
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 […] The post How We Built Our lakeFS Iceberg Catalog appeared first on Git for Data - lakeFS.| Git for Data – lakeFS
Learn about our vision for how to close the AI data infrastructure gap using our funding round to promote enterprise data version control best practices. Read on to learn more.| Git for Data - lakeFS
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 […] The post What is Data Virtualization? Benefits, Use Cases & Tools appeared first on Git ...| Git for Data – lakeFS
Open source software has fundamentally reshaped technology—delivering unmatched flexibility, low friction, and rapid innovation. For some teams, it’s a philosophical commitment. For others, it’s the fastest path to building. lakeFS supports both models. For most data teams, the journey starts with open source and evolves over time. lakeFS open source offers a robust foundation for […] The post The Evolving Equation: When Do You Move From Open Source to Enterprise with Data Version Con...| Git for Data – lakeFS
Despite the increasing adoption of Artificial Intelligence (AI) applications, most organizations are bound to see implementation challenges. One of the issues lies in the data itself. A recent survey showed 80% of companies believe their data is suitable for AI, but more than half are actually dealing with challenges like internal data quality and categorization […] The post AI-Ready Data: Characteristics, Challenges & Best Practices appeared first on Git for Data - lakeFS.| Git for Data – lakeFS
An AI Factory with data versioning doesn't just run smoother. It fundamentally changes how teams interact with their data. Read more.| Git for Data - lakeFS
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; […] The post Git-Like Data Versioning Meets MLOps: lakeFS with MLflow, DataChain, Neptune & Quilt appeared first on Git for Data - lak...| Git for Data – lakeFS
Introducing lakeFS Iceberg REST Catalog, enabling seamless version control for both structured and unstructured data at any scale. Read more.| Git for Data - lakeFS
Introducing lakeFS 1.59.0. Whether you're a seasoned lakeFS user or just getting started, the new UI provides a better experience for your data versioning.| Git for Data - lakeFS
Discover what data discovery is, how it works, its benefits, challenges, and best practices to turn raw data into strategic, actionable insights.| Git for Data - lakeFS
Explore 5 defining trends in the annual State of Data and AI Engineering 2025 report. Uncover what changed and what's trending this year.| Git for Data - lakeFS
Discover what an AI factory is, how it works, and how companies use it to turn raw data into scalable, automated, and intelligent business solutions.| Git for Data - lakeFS
Learn how to build a solid AI infrastructure for efficiently developing and deploying AI and machine learning (ML) applications. Read more.| Git for Data - lakeFS
AI data storage solutions are a key component of the modern AI landscape. Discover benefits, common challenges, and best practices. Read more| Git for Data - lakeFS
Sometimes, you need to step away to see things clearly. Barak shares his story on the path he took to, from, and then back to lakeFS.| Git for Data - lakeFS
Learn what metadata is, its types, benefits, and best practices. Discover how metadata improves data governance, compliance, and AI-driven insights.| Git for Data - lakeFS
ML reproducibility pillars require a disciplined approach to managing input data, code, and execution environments. Read more.| Git for Data - lakeFS
Discover the benefits of CI/CD pipelines, how to implement them and find out how to ensure high quality data pipelines.| Git for Data - lakeFS
Discover the benefits of unit testing for notebooks. Get a step-by-step guide to creating and running a unit test including best practices, tools and examples.| Git for Data - lakeFS
Learn how to get started with data lake implementation. Explore the essentials to enhance your data management strategies.| Git for Data - lakeFS
Learn more about data preprocessing in machine learning and follow key steps and best practices for improving data quality.| Git for Data - lakeFS