The Dremio Blog

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.

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.

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.

The metadata structure of a table format determines everything: how fast queries start planning, how efficiently concurrent writes are handled, how schema changes propagate, and how much overhead accumulates over time.

Polaris is production-ready today. Organizations are already using its RBAC, catalog federation, credential vending, Iceberg SQL views, and generic tables to govern multi-engine lakehouses at scale.

A table format is a specification that defines how to organize metadata about data files so that query engines can treat them as reliable, transactional tables. It sits between the query engine and the physical files.

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.

It is a great thing that so many platforms now support Apache Iceberg. More support means more flexibility for everyone. But if your intention is to make Iceberg your primary analytics format, then "supports Iceberg" and "built for Iceberg" lead to very different outcomes.

The data lakehouse takes a different approach. It deconstructs these components into modular, interchangeable layers, each built on open-source standards. This post walks through the Apache Software Foundation projects that form the core of the open lakehouse stack, what each one does, and how Dremio integrates them into a production-ready platform with built-in AI capabilities.

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.