The Dremio Blog

Learn how agentic workflows for enterprise analytics connect AI agents, governed data and multi-step analysis to improve complex business decisions.

Learn the benefits of agentic analytics and how enterprise teams use natural language queries, governed data and AI agents to improve decisions.

The insurance industry is undergoing a structural shift. What was once a slow moving, data heavy sector is now being reshaped by real time intelligence, automation, and advanced analytics powered by artificial intelligence. Agentic AI is no longer a futuristic concept or a “nice to have” innovation, it is rapidly becoming the competitive baseline that […]

Understanding the write process is critical because it explains why Iceberg can provide ACID guarantees on top of object storage, something that seems impossible when you consider that S3, ADLS, and GCS have no built-in transaction support.

The Agentic Lakehouse is not a new name for the same architecture. It represents a genuine shift in what a data platform is responsible for. A traditional lakehouse is a managed repository. An Agentic Lakehouse is an active participant in AI workflows: it provides context, enforces governance, and optimizes itself autonomously.

Text-to-SQL on a governed semantic layer is significantly more reliable than text-to-SQL on a raw production schema. The semantic layer constrains what the model can access, provides business-friendly terminology, and enforces metric definitions. The accuracy improvement is material.

The convergence of AI agents, open table formats, and semantic tooling is making this architecture decision more consequential than it was a few years ago. AI agents that query through ungoverned raw tables or that cannot discover what data exists are not reliable.

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.

 Semantic layer governance AI is the architectural pattern that closes this gap by enforcing data access controls structurally, at the layer every query must pass through, rather than procedurally, in workflows that agents simply skip.

In data analytics, it's the query engine that gets all the attention. It's where the SQL runs and where the performance story is told. But the storage layer underneath is just as important; it's the "lake" part of the "lakehouse" after all. Choose the wrong storage infrastructure and you're facing I/O bottlenecks no query engine […]

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.