The Dremio Blog

We’re excited to announce Dremio Cloud, The Agentic Lakehouse—the lakehouse built for agents and managed by agents. This milestone marks a major leap forward in Dremio’s evolution, reimagining the modern lakehouse for the agentic era, where intelligent systems collaborate with humans to deliver insights, automate operations, and continuously optimize performance. As organizations accelerate their AI […]

Many data engineers and data analysts spend much of their day in Visual Studio (VS) Code, writing SQL, testing queries, and working with data. Constantly switching between tools disrupts productivity and the user work flow. The VS Code extension for Dremio brings the power of the agentic lakehouse directly into your development environment, enabling you […]

Companies are racing to operationalize agentic AI, but the path from raw unstructured data to an informed decision often breaks down in the final mile. Teams juggle schema knowledge, joins, query tuning, visualization tools, and unified governance checks before they can answer even a simple business question. With Dremio’s Agentic Lakehouse, we remove that friction […]

The rapid growth of the use of AI throughout the modern data stack has transformed how organizations extract insights from their data. With our latest release, we're excited to announce the general availability of AI Functions — a capability that brings the power of Large Language Models (LLMs) directly into SQL execution, making Dremio’s Agentic […]

Discover how Dremio’s Next Generation Cloud and enterprise MCP Server simplify data exploration with AI-driven queries, governance, and natural-language SQL.

Dremio is always striving to abstract away the physical concerns of data, whether the storage location, partitioning schema, or file size optimisation. Thanks to features such as Data Federation, Iceberg Clustering, and Autonomous Performance functionalities, Dremio users get highly-performant access to their data no matter where it lives.  One of the components that delivers this […]

AI data management is the practice of preparing, organizing, governing, and serving enterprise data so it can be used effectively by AI models and agents. It includes collecting data from multiple systems, maintaining high data quality, enforcing governance, and delivering fast, consistent access to that data for training and inference.

AI-ready data is structured, governed, and accessible in a way that supports machine learning, large language models (LLMs), and real-time intelligent agents. Unlike traditional analytics data, it’s not just clean, it’s optimized for rapid, automated decision-making at scale. AI-ready data supports diverse formats, is accessible without ETL, and maintains the context required to train and operate intelligent systems.

Apache Polaris 1.2.0 continues to make the case for a fully open, production-grade Iceberg catalog. These changes reflect real-world needs: better control, stronger security, broader compatibility, and early hooks for observability. As Iceberg adoption grows, Polaris is becoming the default choice for teams who want to avoid vendor lock-in while building modern lakehouse infrastructure. Whether you’re using Dremio Catalog or deploying Polaris yourself, this release brings features that support scale, safety, and flexibility.

Formats like Lance, Nimble, and Vortex show that innovation is accelerating. Each one introduces new ideas about how to structure, index, and access data. But none of them exist in isolation. They need to integrate with engines, catalogs, and governance layers to be truly useful.

The New Economics of Data Cloud data warehouses like Amazon Redshift were built for a world that no longer exists. In that earlier era, organizations focused primarily on structured business intelligence, static dashboards, and predictable workloads. Data was tightly controlled, compute resources were fixed, and dynamic scalability for rapidly changing workloads was not a concern. […]

Running Polaris locally with MinIO gives you a hands-on view of how an open catalog governs Iceberg tables. But when it’s time to move beyond experimentation, deploying and managing a full Polaris environment can take more effort. That’s where Dremio’s integrated catalog comes in. Dremio’s catalog is built directly on Apache Polaris. It delivers all the same open governance, authentication, and interoperability while removing the setup work. You get a production-ready Polaris deployment with a complete user interface, automated security management, and fine-grained access controls, all without maintaining separate services.

Caching dramatically reduces latency and computational costs by storing frequently accessed data closer to where it's needed. Instead of repeated expensive operations - such as fetching from object storage, planning complex queries, or executing SQL - the data you need is provided in fast, local memory. To deliver on this, Dremio implements different layers of […]

The Shift from Warehouses to the Agentic Lakehouse Amazon Redshift has long been a dependable data warehouse for analytics, but the analytics landscape has evolved. Organizations are no longer just running dashboards—they’re powering agentic AI systems that reason, act, and make autonomous decisions based on live business data. These workloads demand real-time responses, high concurrency, […]

This only scratches the surface of Dremio's content. Explore Dremio University and the Dremio Blog to find much more great Dremio Content. Also get involved in the Dremio and OSS community at developer.dremio.com.