The Dremio Blog

The future of AI-driven analytics isn’t about choosing between SQL and semantics, it’s about combining them. Dremio’s MCP server provides the best of both worlds, enabling agents to query, discover, and interpret enterprise data with confidence.

AI will only be as good as the data foundation it stands on. With Dremio’s AI-enabled semantic layer, organizations can unlock the promise of agentic analytics while maintaining governance, consistency, and performance.

The future of analytics isn’t about replacing people with AI, it’s about augmenting decision-making with intelligent, governed access to data. Dremio’s MCP server opens the lakehouse to this new generation of agents, ensuring that insights are not only fast but also accurate, secure, and aligned with business definitions.

For organizations exploring agentic AI, this translates into a critical edge: AI agents can generate dynamic, ad-hoc questions and still receive sub-second, business-ready answers. With reflections, the performance layer is no longer a bottleneck, it becomes an enabler of intelligent, real-time decision-making.

Dremio’s MCP server, integrated semantic layer and autonomous reflections deliver this combination. They turn natural‑language intent into secure, performant and semantically rich analytics, enabling agents to act not just as chatbots but as trustworthy decision‑makers.

Reflections are a query acceleration functionality unique to Dremio, that work by minimising data processing times and reducing computational workloads. Debuting in the early days of Dremio, Reflections accelerate data lake queries by creating optimised Apache Iceberg data structures from file-based datasets, delivering orders-of-magnitude performance improvements. However, the game-changing aspect of this technology was not […]

A promise of self-service data platforms, such as the Data Lakehouse, is to democratise data. The idea is that they empower business users (BUs), those with little or no technical expertise, to access, prep, and analyse data for themselves. With the right platform and tools your subject matter experts can take work away from your […]

Dremio simplifies all of it. By bringing together query federation, an integrated Iceberg catalog, a built-in semantic layer, autonomous performance tuning, and flexible deployment options, Dremio makes it easier to build and run a lakehouse without stitching together multiple tools.

The MCP Server is a powerful alternative to the command line or UI for interacting with Dremio. But can only data analysts benefit from this transformative technology?

Back in April of this year Dremio debuted its own MCP server, giving the LLM of your choice intelligent access to Dremio’s powerful lakehouse platform. With the Dremio MCP Server the LLM knows how to interact with Dremio; facilitating authentication, executing requests against the Dremio environment, and returning results to the LLM. The intention is […]

With traditional setups like Claude Desktop, that bridge is tightly coupled to a specific LLM. But with the Universal MCP Chat Client, you can swap out the brain, GPT, Claude, Gemini, Cohere, you name it, and still connect to the same tool ecosystem.

This guide will equip you with the expertise to easily build an AI-ready data product using Dremio and dbt.

Incremental materializations allow you to build your data table piece by piece as new data comes in. By restricting your build operations to just this required data, you will not only greatly reduce the runtime of your data transformations, but also improve query performance and reduce compute costs.

Why SQL Must Evolve in the Era of Agentic Apps and Data-Aware AI SQL has long been the universal language of data. But with the rise of Generative AI and agentic applications, a major shift is underway. We're entering an era where natural language is the interface, and agents are the client. There are two […]

By leveraging the dbt-dremio adaptor, Analytics Engineers can seamlessly sync model descriptions and tags from dbt projects with Dremio to generate wikis and labels for Business Users and Data Analysts.