The Dremio Blog

The most expensive mistake in data lake querying is the accidental full table scan: a query that reads every file because the user did not correctly reference the partition columns. In Hive, this happens constantly. In Iceberg, it is structurally impossible because users never reference partition columns at all.

The reason so many agentic analytics projects stall at proof-of-concept is not the AI model. It is the absence of the infrastructure that would make the AI trustworthy on real data. A semantic layer is that infrastructure.

The Model Context Protocol (MCP) changes this equation. An MCP server data lakehouse setup gives any compliant AI client a single, governed, structured gateway to your data. You configure it once. Every agent that follows the spec connects automatically.

Solving the Apache Iceberg small files problem requires addressing it at multiple layers. Detection comes first: use table_files() to establish a baseline and set thresholds that trigger action. Prevention comes next: configure write.target-file-size-bytes at the source and increase checkpoint intervals for streaming jobs.

Partition evolution is one of the features that makes Iceberg a safe long-term choice. It means the partitioning decision you make today is not permanent.

From that point, all engines share a consistent view of your Iceberg tables. New tables created by Spark appear in Dremio immediately. Schema changes committed by Flink are visible to PyIceberg clients without any manual sync. The catalog handles the coordination.

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.