The Dremio Blog

Apache Iceberg machine learning workflows are at an inflection point. As AI systems become more autonomous, the requirement to audit what data an AI model was trained on shifts from an engineering preference to a compliance requirement. Financial regulators, healthcare compliance frameworks, and emerging AI transparency mandates are moving toward requiring documentation of training data provenance.

The foundation you built today, a connected source, a semantic layer, a documented catalog, and working AI agent interfaces, is the starting point for all of those capabilities. Each addition builds on what you already have rather than requiring a separate system.

The Iceberg ecosystem is consolidating fast. REST Catalog interoperability, growing AI tooling, and the Apache governance model mean that every month you stay on Delta Lake, you are working against the direction of the industry. The migration investment pays off in engine flexibility, catalog portability, and access to a growing set of tools that assume Iceberg as the standard.

This guide walks you through building a complete Dremio semantic layer for an e-commerce analytics use case from scratch. You will connect raw sources, build three tiers of views, add documentation, apply access control, and verify the whole thing works with Dremio's AI Agent.

Financial services is the industry where a wrong answer from an AI agent doesn't just produce a bad dashboard. It produces a regulatory violation. That single fact changes every architectural decision you make about agentic analytics in banking, insurance, and capital markets.

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 […]

Apache Iceberg 1.11.0 delivers on two fronts. The File Format API is an architectural investment whose full payoff comes over the next year or two as new format plugins ship, but it also consolidates and cleans up the engine's internal format handling today.

Learn what an MCP server is, how it works, and why it powers agentic AI, real-time data access, and scalable workflows for enterprises.

From the original co-creators of Apache Polaris and Apache Arrow, Dremio is the only lakehouse that meets the needs of AI agents and humans through autonomous optimization, a unified semantic layer, and Zero-ETL federation.

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 […]

The challenge of performance in an agentic analytics environment isn't that you have too little control over your query engine. It's that you can't use control you don't have time to exercise. AI agents generate novel queries faster than any human performance review cycle can respond to.

The data lakehouse resolves the core tradeoff that made the warehouse-vs-lake debate so frustrating.

This guide explores what semantic layers are, their benefits and how they’re implemented within your enterprise data stack.