The Dremio Blog

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.

Learn how to build a customer 360 dashboard that unifies customer data and see how Dremio powers scalable, AI-ready analytics for enterprises.

Explore the nine best agentic analytics tools for data analysis in 2026, and learn why Dremio is the top solution for enterprise users.

Discover how analytics AI agents drive faster decisions when powered by a governed, scalable lakehouse foundation built for enterprise data.

The safest data architecture is one where data doesn't move, policies are unified in a central catalog, and every query is authenticated, authorized, and encrypted. By abstracting the complexity of data access and enforcing fine-grained controls at the catalog level, Dremio secures the data foundation so your teams—and your AI agents—can explore insights freely.

Dremio's Agentic Lakehouse provides an alternative for the workloads that drive the highest Databricks spend: interactive analytics, BI dashboards, and ad-hoc queries. By offloading these queries to Dremio's engine with Autonomous Reflections, you eliminate the DBU consumption and the underlying cloud compute for 60-80% of your analytical workload. Meanwhile, Databricks stays in place for the heavy processing it does well: ETL pipelines, ML training, and Spark-based transformations.