lakeFS Cloud with a Git-like Version Control Interface
Improve data lake management with our Git-like version control interface. No deployment or scaling hassles. Start a free trial!| Git for Data - lakeFS
Improve data lake management with our Git-like version control interface. No deployment or scaling hassles. Start a free trial!| Git for Data - lakeFS
Discover big data testing benefits and challenges. Explore strategies including data sampling, production data handling, and versioning for reliable pipelines.| Git for Data - lakeFS
Explore data governance frameworks, their pillars, benefits, and challenges. Learn how to protect data quality, access, compliance, and integration.| Git for Data - lakeFS
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
What is the difference between lakeFS and open table formats (OTF), namely Apache Iceberg, DeltaLake and Apache Hudi.| Git for Data - lakeFS
A majority of data architectures feature Hive Metastore. Why has it survived and what can finally replace it in the future?| Git for Data - lakeFS
Explore 6 types of metadata with examples, tools, and frameworks to boost data discovery, governance, quality, and collaboration.| Git for Data - lakeFS
Explore how to achieve effective AI metadata management with lakeFS. Learn best practices and real-world use cases to simplify metadata handling.| Git for Data - lakeFS
Enhance your data security in lakeFS by using Role-Based Access Control (RBAC) to ensure specific user roles have appropriate access to data.| Git for Data - lakeFS
Review data across multi‑cloud & on‑prem—from governance and cost challenges to collaboration hurdles—why distributed strategies fall short.| Git for Data - lakeFS
Explore the top 12 data science tools in 2025, featuring Python, Power BI, TensorFlow and find out how these tools can help you expedite your AI/ML projects.| Git for Data - lakeFS
Learn how to solve AI infrastructure challenges in regulated sectors and innovate confidently in today’s rapidly evolving AI landscape.| Git for Data - lakeFS
New York, NY, July 29, 2025, – lakeFS, the leading “git-for-data” version control system for enterprise data and AI initiatives, has raised $20 million in a growth funding round. With thousands of organizations including Arm, Bosch, Lockheed Martin, NASA, Volvo, and the U.S. Department of Energy already using lakeFS as part of their data management […]| Git for Data - lakeFS
Tailor-made for data scientists and machine learning practitioners, lakeFS Mount simplifies workflows with seamless integration. Read on to learn how.| 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
Discover the importance of data mesh for data engineers. Learn how software engineering best practices can revolutionize data management.| Git for Data - lakeFS
Learn what a data quality framework is, why it matters, and how to implement it to ensure accurate, reliable, and trustworthy data for your business.| Git for Data - lakeFS
RAG combines LLMs with information retrieval systems. Explore top RAG tools and learn how to choose the best one for your specific use case.| Git for Data - lakeFS
In the annual State of Data Engineering 2024, we explore three defining trends in this space. Find out the results in this year's report.| Git for Data - lakeFS
What is RAG as a Service? Discover core components, common use cases, challenges and best practices. Read on to learn more.| Git for Data - lakeFS
Explore data pipeline automation and boost business growth through enhanced data quality, efficiency, and scalability. Learn how to streamline data management.| Git for Data - lakeFS
Explore data engineering trends, dive into machine learning tutorials & learn the best practices on how to manage data with lakeFS.| 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
Discover how data catalogs enhance data management, quality, and insights. Learn about top 26 data catalogs, their features, and benefits.| Git for Data - lakeFS
Explore how data manageability and Git-like tools are transforming data trust, discovery, and resilience in the modern open data stack.| Git for Data - lakeFS
Discover the top data lineage tools for 2025 and learn how they improve data management, compliance, and troubleshooting for your organization.| 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
Follow these 16 actionable strategies that will help you improve data quality across your entire organization. Read on to learn more.| Git for Data - lakeFS
Discover the most common data quality issues and how to fix them. Explore important data quality checks and tools that solve these issues.| Git for Data - lakeFS
Learn how lakeFS Mount optimizes deep learning workloads by improving object storage performance. Discover how it integrates with data version control systems.| Git for Data - lakeFS
Discover how Hudi, Iceberg, and Delta Lake compare in data lake table formats, focusing on performance, scalability, updates, and platform compatibility.| 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
Why is testing data pipelines so important? Find out how to implement the right test and learn how to overcome common testing challenges.| Git for Data - lakeFS
Deep dive into the design of lakeFS on the rocks: how we chose layout and sizes of Pebble SSTable files on S3.| Git for Data - lakeFS
Learn how effective metadata management can enhance data lake usability to match database experiences. Explore the challenges and solutions for data teams.| Git for Data - lakeFS
Discover how multiple storage backends support in lakeFS provides a capability that unifies data management across all your storage systems.| Git for Data - lakeFS
This guide takes a look at technological innovations and processes that are changing the future of data analytics and analytical data processing systems.| Git for Data - lakeFS
Ensure optimal data quality in your business with key strategies in data quality management. Learn to enhance data fitness for informed decision-making.| Git for Data - lakeFS
Learn how to achieve lineage quickly at minimum cost, using data version control concepts you are already familiar with from managing code.| Git for Data - lakeFS
Find out what object storage is, why you should use it, and how to integrate it in your application.| Git for Data - lakeFS
Explore the most popular AI frameworks. Learn about open-source vs. commercial options, key features, and benefits to accelerate AI development.| Git for Data - lakeFS
What role does AI in data engineering stand to play in enabling best practices? Keep reading to learn how data engineers benefit from AI 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
If you have any questions, want to contribute or require technical support, we're here to help. Click here to get in-touch with the team.| 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 what an Online Transaction Process (OLTP) database is, how it works, plus a handful of best practices for building efficient OLTP systems.| Git for Data - lakeFS
Databricks Unity Catalog is a uniform governance solution for data & AI assets in your lakehouse. Check our guide on streamlining data assets| Git for Data - lakeFS
Find out what a data lake is, how it's different from the data warehouse, explore its features, and learn how to build it!| Git for Data - lakeFS
Explore the leading tools and trends that shaped data engineering in 2023. Read the detailed report on data version control at scale.| Git for Data - lakeFS
Explore how to test data validity and accuracy. Learn about data quality dimensions, and discover data quality testing frameworks.| Git for Data - lakeFS
What is metadata? Why is it so important? Keep reading to learn more about modern practices in metadata management.| Git for Data - lakeFS
Data is the foundation for decisions in many organizations. This article overviews how to maintain data quality in the data lake.| Git for Data - lakeFS
Dive into data quality: Discover best practices, gain insights on top tools, and see how data version control boosts reliability| Git for Data - lakeFS
How to implement Write-Audit-Publish (WAP) on Apache Iceberg, Apache Hudi, Delta Lake, Project Nessie, and lakeFS| Git for Data - lakeFS
Planning to integrate lakeFS with Databricks? Here is a step by step tutorial to help you integrate them quickly and easily.| Git for Data - lakeFS
Discover top Jupyter Notebook alternatives for 2025. Find the best tools for collaboration, data visualization, and seamless integration.| Git for Data - lakeFS
Explore the top data version control tools (DVC tools) that data practitioners use to solve their data challenges in 2025.| Git for Data - lakeFS
Explore data version control best practices, from picking the right data versioning tool to smart management of data and version expiration.| Git for Data - lakeFS
This blog explains the concept of Write-Audit-Publish, which is a pattern in data engineering to enforce data quality in data pipelines.| Git for Data - lakeFS
Explore how prioritizing data governance unlocks data's full potential for a competitive advantage in a data-driven world.| Git for Data - lakeFS
Uncover the benefits of data version control. Understand what it is, how it works, and why it's essential for data engineers| 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
Discover the key elements of ML architecture and their representation in the form of a machine learning architecture diagram| Git for Data - lakeFS
A common question we encounter is "where is my data"? Find out the steps lakeFS take to hide data and how this core functionality works.| Git for Data - lakeFS
Learn more about Databricks architecture and how it can help your team harness the potential of data in your organization.| Git for Data - lakeFS
Get a primer on machine learning architecture and see how it enables teams to build strong, efficient, and scalable ML systems.| Git for Data - lakeFS
Discover best practices for preparing machine learning data. Learn how to optimize your ML projects with effective data preparation techniques.| Git for Data - lakeFS
Find out how Databricks Autoloader can help you create a scalable, reliable, and stable data intake pipeline. Read on to learn more.| Git for Data - lakeFS
Explore how to add data versioning to an ML project using lakeFS-spec: an easy way to work with lakeFS from Python. Read on to learn more.| Git for Data - lakeFS
Explore top data quality tools for 2025, their benefits, and key metrics to track for better decision-making, compliance, and productivity.| Git for Data - lakeFS
Learn more about data versioning and find out why it's important. Follow best implementation strategies and check out data versioning examples and use cases.| Git for Data - lakeFS
This article dives into Databricks: what it is, how it works, its core features and architecture, and how to get started. Read more| Git for Data - lakeFS
ETL testing is vital for ensuring data integrity and preventing costly errors. Learn the best practices and discover 8 stages of ETL testing process.| Git for Data - lakeFS
With data security concerns on the rise, lakeFS offers a compelling solution for pre-signed URLs to safeguard critical data assets. Read on.| Git for Data - lakeFS
Let's dive into LangChain in detail to show you how it works, what developers can build with it, and how it fits into ML architectures.| Git for Data - lakeFS
Databricks SQL: A tool for data analysis & collaboration. Explore its features, BI integrations, & optimization techniques.| Git for Data - lakeFS
Data scientist, ML engineer, or AI enthusiast? This guide teaches you to harness parallel ML effectively in 2025| Git for Data - lakeFS
Learn about lakeFS’s garbage collection capabilities, designed to handle large-scale data environments and keep your data lake clean and organized.| Git for Data - lakeFS
lakeFS now supports the ability to locally checkout paths from your repository for flexible and scalable data version control.| Git for Data - lakeFS
