The Dremio Blog

Discover how AI-ready data drives accuracy, scalability, and efficiency—and how Dremio’s Intelligent Lakehouse simplifies the entire process.

A truly modern data lakehouse is defined by more than just its use of open formats. Its value is measured by its intelligence and completeness. It is a platform where AI can take action, where openness extends to other compute engines, where the semantic layer is a structured and programmable asset, and where performance management is autonomous. These integrated capabilities are shifting the baseline for what organizations should expect from a data platform. The focus is no longer just on storing and querying data, but on creating a self-managing, intelligent, and truly unified ecosystem. As you evaluate your data strategy, ask yourself a forward-looking question: As AI becomes a more active participant in our data ecosystems, how will you leverage it to not just analyze your business, but to help run it?

Dremio's AI Functions are more than just a new feature; they are a bridge over the long-standing divide between unstructured data and SQL-based analytics. By embedding LLM capabilities directly into the query engine, Dremio provides a complete workflow to unlock your most inaccessible data. You can now analyze data where it lives, choose the best AI model for the job, manage workloads with enterprise-grade controls, and transform raw files into governed, high-performance data products. The era of inaccessible information is over.

The data lakehouse is evolving beyond just being a repository for data. With Dremio, it's becoming an autonomous, open, and intelligent platform that actively works to simplify your architecture, accelerate your queries, and expand the very definition of what data can be analyzed.

These four capabilities are not just a list of features; they are a reinforcing system. The semantic layer provides the context, federation provides the reach, autonomous optimization delivers the speed, and the MCP framework enables the final, crucial step: action. This is the complete blueprint for agentic analytics.

Apache Polaris 1.3.0 shows steady progress in how the project handles security, storage, events, and development workflow. The release tightens access control, expands format coverage, and offers clearer audit signals. It also brings faster builds and cleaner client tools. Each change supports a stable and flexible catalog that can serve many engines and many teams.

Companies are racing to operationalize agentic AI, yet the final process of getting from data to decision is extremely difficult,  requiring data integration, tuning, and governance management. With Dremio’s latest release, we remove these blockers by putting natural‑language intelligence, explainability, and self‑optimizing performance directly into the lakehouse experience. You get clarity and control without copies, […]

We're excited to announce the introduction of Service Users in Dremio—a new authentication approach designed specifically for the agentic AI era, supporting machine-to-machine (M2M) applications and automated systems. Service users provide a more secure and streamlined way to integrate AI agents,  applications, scripts, and CI/CD pipelines with your Dremio environment. What Are Service Users? Service […]

We’re excited to share, in partnership with Microsoft, that Dremio is the first agentic lakehouse platform to fully support the open source Apache Arrow Database Connectivity (ADBC) driver for Power BI, bringing next-generation performance to your analytics. Whether you’re working with Dremio Cloud or Dremio Software, this enhancement is available across Power BI Desktop, Power […]

With lakeFS, you version everything: Iceberg tables, images, models, logs. With Dremio, you query and analyze it all, structured or not, at scale. Together, they bring Git-style control and interactive querying to your data lake, so you can build more intelligent, version-aware workflows without sacrificing flexibility or performance.

In this blog you will learn how to connect each tool to a REST catalog like Dremio Catalog, using bearer tokens and vended credentials to keep your pipelines secure and portable.

Dremio has introduced several capabilities that inteliigently improve query performance across the data lakehouse. With minimal to no action from users, Dremio will reduce query latency, handle data maintenance tasks, and eliminate redundant compute jobs. This article is a summary of three of these performance management features. Read on to learn how reflections accelerate popular […]

The Intelligent Lakehouse for the Agentic AI Era Data teams today face a familiar challenge: how to unlock value from ever-growing, scattered data without the delays and cost of traditional ETL pipelines. Together, HPE Alletra Storage MP X10000 and Dremio’s Intelligent Lakehouse Platform solve this problem—combining HPE’s flash-optimized performance with Dremio’s open, unified query and […]

You can move unstructured content into Iceberg tables with AI functions. You can use Dremio’s integrated AI agent for natural language exploration. You can connect external assistants through MCP to build multi-step workflows. All these pieces work together. They give data teams a clear path from raw information to AI-powered insights that stay accurate and trustworthy.

Dremio Next Gen Cloud represents a major leap forward in making the data lakehouse experience seamless, powerful, and accessible. Whether you're just beginning your lakehouse journey or modernizing a complex data environment, Dremio gives you the tools to work faster and smarter—with native Apache Iceberg support, AI-powered features, and a fully integrated catalog. From federated queries across diverse sources to autonomous performance tuning, Dremio abstracts away the operational headaches so you can focus on delivering insights. And with built-in AI capabilities, you're not just managing data—you’re unlocking its full potential.