Apache Polaris provides the open foundation, but enterprises running enterprise-wide, AI-driven platforms need more than a standards-compliant catalog service. They need enterprise governance, automation, and business context that AI systems can reason over.
As original co-creator of Apache Polaris and one of the project’s largest contributors, Dremio uses Polaris as the foundation of Dremio’s Open Catalog and extends it into a full enterprise control plane for the Iceberg lakehouse. Dremio’s Open Catalog manages technical metadata while also serving as the system of record for business meaning, and governance.
Traditional catalogs focus on schemas, partitions, and file locations. That information is essential, but it is no longer sufficient in an agent-driven world.
AI agents need to understand:
Dremio’s Open Catalog stores both the technical metadata provided through Polaris and the business context captured by Dremio’s AI Semantic Layer. This includes domain concepts, metrics, relationships, and governed definitions that describe how data should be interpreted.
When agents interact with the platform, they draw on this semantic foundation to select the right datasets, comply with policies, and generate explainable results. The catalog becomes not just a registry of tables, but the knowledge base that powers trustworthy analytics and automation.
Explore this interactive demo and see how Dremio's Intelligent Lakehouse enables Agentic AI
Dremio’s Open Catalog is much more than a managed Polaris service. It combines open standards with enterprise-grade functionality that turns metadata into a strategic business asset.
Enterprise governance at scale
Robust data governance such as fine-grained access controls, policy enforcement, and lineage tracking allow organizations to safely operate across clouds, regions, and regulatory environments.
Automated optimization Iceberg Clustering automatically optimizes Iceberg table layouts to accelerate query performance. Clustering organizes data so similar values are stored together, enabling the query engine to skip irrelevant data files and dramatically reduce scan times without requiring manual table tuning or maintenance.
Automated maintenance Dremio handles critical Iceberg table maintenance operations automatically, including compaction to optimize file sizes, vacuum to remove expired snapshots and orphaned metadata, and continuous repartitioning. These tasks run autonomously, eliminating operational overhead and keeping lakehouse environments healthy.
Federated visibility across the data estate
In addition to Iceberg tables managed through Polaris, Dremio catalogs and governs data that lives in warehouses, databases, and object stores, giving teams a unified view of trusted enterprise data.
Built for agentic AI
The tight integration between Open Catalog and the AI Semantic Layer allows organizations to expose governed business meaning directly to agents. This enables autonomous systems to move beyond querying tables and toward driving real operational workflows.
Together, these capabilities transform Apache Polaris from an open catalog foundation into the backbone of an enterprise-ready, AI-native data architecture.
Deploy agentic analytics directly on Apache Iceberg data with no pipelines and no added overhead.
By unifying data from diverse sources, simplifying data operations, and providing powerful tools for data management, Dremio stands out as a comprehensive solution for modern data needs. Whether you are a data engineer, business analyst, or data scientist, harnessing the combined power of Dremio and Apache Iceberg will undoubtedly be a valuable asset in your data management toolkit.
This blog helps you learn about table-driven access policies in Dremio Cloud and Dremio Software v24.1+.
Dremio Arctic bring new features to Dremio Cloud, including Apache Iceberg table optimization and Data as Code.
