The Dremio Blog

Partition evolution is one of those features that seems minor until you need it. Then it's the difference between a two-minute metadata update and a two-day rewrite project. If you're building on Iceberg and haven't thought carefully about your partition strategy yet, the time to do that is before your table reaches 10 TB, not after.

The framing that matters here: agentic analytics is not a feature you add to your existing BI stack. It is a different approach to how analytical work gets done, who does it, and at what speed.

The Agentic Lakehouse is not a different architecture from your existing lakehouse. It is four additional structural layers built on top of a foundation you have likely already built: an AI Semantic Layer, Autonomous Performance, active metadata, and agent-specific interfaces.

The release of Apache Polaris 1.5.0 marks a significant step forward in the project's evolution. This release introduces enterprise-grade security integrations, expanded catalog federation, advanced credential vending, and key performance optimizations.

This composability is what makes the Agentic Lakehouse architecture viable long-term. As Iceberg V3 adoption grows and the Polaris REST Catalog becomes the universal standard for catalog interoperability, adding a new engine or a new AI framework to your stack becomes a configuration change, not a migration project.

The single biggest performance advantage of Iceberg over raw data lakes is not a clever algorithm or a faster codec. It is metadata-driven data skipping. By the time a query engine begins scanning actual Parquet files, Iceberg's metadata has already eliminated 90-99% of the files from consideration.

Apache Iceberg V3 is a meaningful advancement over V2, not a version bump for its own sake. Deletion vectors address the fundamental I/O cost of merge-on-read that V2 delete file accumulation creates. The Variant type eliminates one of the most common workarounds in modern data pipelines: storing JSON as strings and parsing at query time.

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.