The Dremio Blog

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.

AI-ready data is structured, governed, and accessible in a way that supports machine learning, large language models (LLMs), and real-time intelligent agents. Unlike traditional analytics data, it’s not just clean, it’s optimized for rapid, automated decision-making at scale. AI-ready data supports diverse formats, is accessible without ETL, and maintains the context required to train and operate intelligent systems.

Apache Polaris 1.2.0 continues to make the case for a fully open, production-grade Iceberg catalog. These changes reflect real-world needs: better control, stronger security, broader compatibility, and early hooks for observability. As Iceberg adoption grows, Polaris is becoming the default choice for teams who want to avoid vendor lock-in while building modern lakehouse infrastructure. Whether you’re using Dremio Catalog or deploying Polaris yourself, this release brings features that support scale, safety, and flexibility.

Formats like Lance, Nimble, and Vortex show that innovation is accelerating. Each one introduces new ideas about how to structure, index, and access data. But none of them exist in isolation. They need to integrate with engines, catalogs, and governance layers to be truly useful.