The Dremio Blog

For most of its history, the semantic layer was considered a solved problem. You built it once, business users queried it wherever it lived, and (hopefully) everyone would agreed on what "revenue" meant. However, much like the information in your data dictionary, the popularity of the semantic layer went stale and businesses turned to new, […]

Data pipelines used to require a lot of infrastructure to keep running: separate compute for transformation, staging layers between systems, and a growing stack of tools to manage it all. Dremio changes the equation. With native ingestion, flexible transformation, and AI-assisted pipeline development, teams can build and operate end-to-end ELT workflows directly in the lakehouse, […]

Most conversations about AI and data platforms start with MCP. That's understandable: the Model Context Protocol has become the standard way to give AI agents a window into a data system, and Dremio's MCP server does this well. But MCP solves the specific problem of giving agents a supervised, conversational interface to your data. What […]

Learn the benefits of agentic analytics and how enterprise teams use natural language queries, governed data and AI agents to improve decisions.

The insurance industry is undergoing a structural shift. What was once a slow moving, data heavy sector is now being reshaped by real time intelligence, automation, and advanced analytics powered by artificial intelligence. Agentic AI is no longer a futuristic concept or a “nice to have” innovation, it is rapidly becoming the competitive baseline that […]

 Semantic layer governance AI is the architectural pattern that closes this gap by enforcing data access controls structurally, at the layer every query must pass through, rather than procedurally, in workflows that agents simply skip.

In data analytics, it's the query engine that gets all the attention. It's where the SQL runs and where the performance story is told. But the storage layer underneath is just as important; it's the "lake" part of the "lakehouse" after all. Choose the wrong storage infrastructure and you're facing I/O bottlenecks no query engine […]

A lakehouse without governance is a liability. Sure, you can query it, but can you trust it? Analysts find tables with no owner, no description, and no clear indication of whether what they're looking at is current. Likewise, compliance teams can't demonstrate data lineage and engineers can't assess the impact of a schema change before […]

Business intelligence tools are where data stops being infrastructure and starts being useful. Executives review performance in dashboards, product teams track metrics in reports, and finance runs variance analysis against actuals. In each case, the value only materialises if the connection between the tool and the underlying data is fast, reliable, and consistent. Dremio connects […]

Life sciences analytics teams know the dynamic well: the question takes five minutes to ask and six weeks to answer. By the time the extract is ready, the interim analysis window has passed, the formulary negotiation is over, or the adverse event report is already pressing the 15-day FDA deadline. This is the default operating […]

The results are in. In BARC The Data Fabric Survey 26, one of the most rigorous independent evaluations of data platform software in the world, Dremio earned 19 top rankings and 4 leader positions in the Data Platforms peer group. In feedback collected from Dremio users, 100% said they would recommend Dremio, 100% rated their […]

In SaaS data analytics, the gap between the question and the answer can determine whether a product decision gets made this week or next quarter. Your Customer Success team wants to know which accounts are drifting toward churn. Your RevOps lead wants to know where expansion signals are strongest. Your product team wants to understand […]

A lakehouse is only as useful as the data inside it. Query performance, governance, and semantic layers all depend on one assumption: that the underlying data is accurate, complete, and behaving as expected. When it isn't, dashboards return wrong answers, AI agents reason from bad inputs, and engineering teams spend days diagnosing problems that should […]

We have news. The Gartner® Market Guide for Agentic Analytics, published February 9, 2026, maps the platforms shaping an emerging category: software that applies AI agents across the data-to-insight workflow. Among approximately 37 vendors listed, Dremio is recognized for its Agentic Lakehouse Platform as a Representative Vendor. We believe this reflects the data foundation we […]

Using Claude Code to Build an Iceberg Lakehouse For years, building a production-grade data lakehouse required a specialized team: data engineers to design pipelines and to tune queries, and platform architects to manage table maintenance. Apache Iceberg changed the storage and table format equation, giving teams an open, vendor-neutral foundation for any scale of data. […]