The Dremio Blog

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.

Taken together, these changes show a project that is tightening its foundations while expanding its scope. Polaris 1.3.0 improves visibility through metrics, strengthens governance through externalized policy, and broadens catalog coverage through generic tables.

In this blog you will learn how to connect each tool to a REST catalog like Dremio Catalog, using bearer tokens and vended credentials to keep your pipelines secure and portable.

You can move unstructured content into Iceberg tables with AI functions. You can use Dremio’s integrated AI agent for natural language exploration. You can connect external assistants through MCP to build multi-step workflows. All these pieces work together. They give data teams a clear path from raw information to AI-powered insights that stay accurate and trustworthy.

AI data management is the practice of preparing, organizing, governing, and serving enterprise data so it can be used effectively by AI models and agents. It includes collecting data from multiple systems, maintaining high data quality, enforcing governance, and delivering fast, consistent access to that data for training and inference.