The Dremio Blog

An Iceberg table that works well on day one will not work well on day 365 without maintenance. Every append, update, and delete operation adds files and metadata.

Apache Parquet is the recommended file format used in every modern data platform, and for good reason. But what are those reasons? And would it really matter if you stuck with CSV? The short answer is "YES". The slightly longer answer is "Yes, because columns". The full answer is below, so keep on reading to […]

This article examines a newer approach: embedding the catalog directly inside the storage layer. Traditional Iceberg architectures have three components: the query engine, a standalone catalog, and object storage.

For most of its history, the semantic layer was considered a solved problem. You built it once, business users queried it wherever it lived, and (hopefully) everyone would agreed on what "revenue" meant. However, much like the information in your data dictionary, the popularity of the semantic layer went stale and businesses turned to new, […]

Data pipelines used to require a lot of infrastructure to keep running: separate compute for transformation, staging layers between systems, and a growing stack of tools to manage it all. Dremio changes the equation. With native ingestion, flexible transformation, and AI-assisted pipeline development, teams can build and operate end-to-end ELT workflows directly in the lakehouse, […]

Most conversations about AI and data platforms start with MCP. That's understandable: the Model Context Protocol has become the standard way to give AI agents a window into a data system, and Dremio's MCP server does this well. But MCP solves the specific problem of giving agents a supervised, conversational interface to your data. What […]

A lakehouse catalog is the component that answers one question: "Where is the current metadata for this table?" Without a catalog, every engine would need to independently locate and track metadata files. With a catalog, there is a single source of truth that coordinates reads, writes, and access control across all engines.

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.