The Dremio Blog

By combining open standards, autonomous lakehouse operations, and agentic interaction, Dremio turns the journey from raw data to trusted insight into an incremental, flexible process, not a long, disruptive platform migration.

Large Language Models (LLMs) are capable of understanding and generating language. However, they do not understand or process language in the same way that a person like you or I does.  When reading text, humans construct meaning by processing the syntax of each sentence as it unfolds. This involves combining word definitions, using context from […]

The traditional barriers between complex, distributed data and clear, actionable answers have been removed. With an intelligent, AI-native data lakehouse, you no longer need to be a data engineer to explore your data; you just need to be curious.

Discover the top AI tools for data engineers. Compare leading platforms and see how Dremio automates pipelines and boosts performance at scale.

Explore 18 strategies for optimizing SQL queries and learn how enterprises improve speed, reduce compute costs, and maintain efficient workloads with Dremio.

Dremio Reflections don't just accelerate queries; they accelerate the entire analytics lifecycle by removing performance tuning from the critical path. By reclaiming the time and cognitive overhead traditionally spent on manual materialization management, Reflections empower data teams to shift their focus from infrastructure management to insight delivery.

The semantic layer has evolved far beyond its origins as a simple data dictionary. This evolution is marked by five critical shifts: a scalable layered architecture, a focus on serving AI as a primary consumer, a hybrid virtual-physical model for performance, universal federation into a single pane of glass, and governance that is built-in, not bolted-on. It now serves as the critical interface not just for human analysts using BI tools, but for a new generation of AI agents that rely on its rich semantic context to explore, analyze, and generate insights from data.

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.

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 […]