The Dremio Blog

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. […]

Most analytics workloads are fine with millisecond timestamps. A daily sales report doesn't care whether a transaction landed at 14:32:07.001 or 14:32:07.001423. But for the teams that do care about sub-millisecond precision, the lakehouse has historically been a weak link. Timestamps either got truncated on ingestion, stored as strings to preserve precision, or shoehorned into […]

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Adding a column to a large production table used to require a plan. You'd write the migration script, schedule a maintenance window, kick off a backfill job that rewrote every data file to include the new column, and then wait. For a table with billions of rows on a busy lake, that wait could stretch […]

Something I keep hearing in conversations with customers and prospects right now is a version of the same frustration: we have executive buy-in, budget approved, models selected, and we still cannot ship anything meaningful. The AI initiative is stalled, and everyone is looking at the data team. Then there are the teams who are already […]

Your organisation has probably already had the "AI agents" conversation. Maybe it was at a board meeting, maybe it surfaced during quarterly planning, or maybe a team came to you with a proposal and a timeline. Either way, the conversation almost certainly centred on the AI: which model, which vendor, which use case. Very few […]