The Dremio Blog

The framing that matters here: agentic analytics is not a feature you add to your existing BI stack. It is a different approach to how analytical work gets done, who does it, and at what speed.

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.

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.

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.

Dremio is not a traditional data warehouse. It is a unified platform that eliminates data silos through a federated query engine, secures your object storage with an Iceberg-based lakehouse, and accelerates insights with an Agentic AI layer.

The semantic layer is not a one-time project. It is a living system that grows with your organization's data needs. Start small, prove value on the metrics that matter most, and expand from there.

Dremio eliminates that choice. Connect your sources, build your semantic layer, enable AI access, and start migrating to Iceberg when you are ready.

This post walks through the four capabilities that make Dremio the easy button for building a unified, governed, AI-native data platform.

Governed Agentic Access gives agents a safe, fast, purpose-built interface to the platform. Access controls extend cleanly to agent workloads. MCP removes integration friction. AI SQL Functions bring unstructured data analysis into the same query layer. Autonomous Reflections ensure that governance overhead doesn't come at the cost of prohibitive latency.

Apache Iceberg, the open and interoperable table format that has become the industry standard for the modern data lakehouse, adds meaningful new capabilities in Iceberg version 3 (V3). With the March release of Dremio Cloud, those capabilities are now available in all regions. The release brings multiple support for multiple areas: This post covers what's […]

The investment in the semantic layer pays off not just in agent accuracy but in the reliability of every downstream workflow that depends on agent output.

For data engineers building the foundation for agentic analytics, this open-standards approach also means less lock-in risk. The investment in modeling data as Iceberg tables is portable. The catalog is accessible to any Iceberg-compatible engine.

If agentic analytics is on your roadmap, or if you're already building AI applications that need to connect to enterprise data, it's worth auditing where your current platform sits across these three pillars. Most gaps show up fastest when agents start hitting data quality issues, permission errors, or ambiguous schema definitions that a human analyst would have talked their way around.