The Dremio Blog

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.

Today, we're excited to announce the general availability of Dremio's latest release, delivering accelerated AI and analytics through intelligent automation. Marking the next generation of Dremio, this release represents a significant milestone in our mission to eliminate technical complexity and resource waste through autonomous capabilities, empowering teams to innovate rather than maintain. In today's economic […]

Is Query Performance Slowing Down Your AI and Analytics Initiatives? Slow analytics and AI workloads frustrate users and delay critical insights, draining productivity. If waiting for queries to load feels like the norm, you're not alone. But what if query performance could be accelerated—automatically, without requiring any specialized expertise or manual intervention? Enter Autonomous Reflections. […]

Companies of all sizes now use lakehouse architectures to power their analytics and AI workloads. Lakehouses give companies a single, trusted source of data for analytics and AI tools to access, and eliminate the need for data duplication and vendor lock-in. The catalog, or metastore, is an integral part of the lakehouse that enables tools […]

Simplifying Kubernetes Management for the Modern Data Team Enterprise customers leveraging Dremio on Kubernetes (K8s) have long valued the ability to scale engines according to their performance and cost requirements. However, until now, this capability required specialized Kubernetes expertise and manual configuration processes that added complexity to your data infrastructure management. Today, we're excited to […]

Organizations generate vast amounts of data, spanning multiple sources, tables, views, and scripts. Traditional keyword-based search methods often fall short, returning irrelevant results and making data discovery and exploration difficult and time consuming. With AI-Enabled Semantic Search, discovering relevant data to help answer questions and solve business problems becomes intuitive, simple, and quick. AI-Enabled Search […]

Unlocking Effortless Data Organization with Dremio’s Iceberg Clustering Organizations today face significant challenges optimizing their data lakes for performance while minimizing engineering overhead. That's why Dremio is excited to introduce Iceberg Clustering, a powerful capability that intelligently optimizes the data layout in your Apache Iceberg lakehouse.. With Iceberg Clustering, Dremio automatically reorganizes data within partitions, […]

You're not alone if you're currently stretching SQL Server—or any OLTP database—beyond its intended purpose to keep up with analytics demand. This pain point is shared by countless organizations as data volumes grow, dashboards become more complex, and business expectations rise.

By leveraging the Dremio and dbt 's capabilities and adhering to these best practices, business units can build a strong CI/CD pipeline that improves both the quality and speed of data transformation projects.

With Dremio + LangChain + Flask/FastAPI, you can build the next generation of AI-driven applications that provide real-time, data-powered insights.