The Dremio Blog

Artificial Intelligence (AI) has become essential for modern enterprises, driving innovation across industries by transforming data into actionable insights. However, AI's success depends heavily on having consistent, high-quality data readily available for experimentation and model development. It is estimated that data scientists spend 80+% of their time on data acquisition and preparation, compared to model […]

This post kicks off our 10-part series exploring how AI evolved into LLMs, how to enhance their capabilities, and how the Model Context Protocol (MCP) is shaping the future of intelligent, modular agents.

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.

Integrating row column access control with materializations enables Dremio Reflections to deliver high-performance query execution without compromising on security or flexibility, making it an ideal solution for scalable, secure data access in the lakehouse architecture. Furthermore, by enabling pre-compute materializations to be re-usable across users and roles, significant cost savings can be achieved through more efficient engine resource utilization.

At Dremio, we implemented Autonomous Reflections in our own internal Data Lakehouse. We are happy to report that Autonomous Reflections exceeded our expectations. In just days, we saw significant improvements

Credential vending support in Dremio opens up a more secure and convenient way to query external Iceberg catalogs. By obtaining temporary, table-scoped credentials on the fly, Dremio minimizes long-lived secrets and ensures access is tightly controlled by the catalog’s policies.

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, […]

In this tutorial, we’ll walk you through building a beginner-friendly MCP server that acts as a simple template for future projects. You don’t need to be an expert in AI or server development—we’ll explain each part as we go.

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.