The Dremio Blog

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.

Discover leading AI-powered data analytics solutions and see how they enhance insights, automation and enterprise decision-making.

Explore the key differences between Delta Lake and Iceberg, and learn how open data formats enable scalable, AI-ready lakehouse architectures.

Explore how a universal semantic layer can unify your data sources, simplify analytics, and help you achieve better insights.

Explore how data unification works, discover leading, and learn how Dremio can help your enterprise drive better business insights.

Agentic analytics isn’t a trend. It’s the next phase of how organizations work with data. AI agents need access, speed, and context, across every system your business relies on.