The Dremio Blog

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.

Using Claude Code to Build an Iceberg Lakehouse For years, building a production-grade data lakehouse required a specialized team: data engineers to design pipelines and to tune queries, and platform architects to manage table maintenance. Apache Iceberg changed the storage and table format equation, giving teams an open, vendor-neutral foundation for any scale of data. […]

Most analytics workloads are fine with millisecond timestamps. A daily sales report doesn't care whether a transaction landed at 14:32:07.001 or 14:32:07.001423. But for the teams that do care about sub-millisecond precision, the lakehouse has historically been a weak link. Timestamps either got truncated on ingestion, stored as strings to preserve precision, or shoehorned into […]

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.

Explore leading AI and ML data integration services, learn selection criteria and see how Dremio powers agents and automation

Explore the 11 best alternatives to Hadoop in 2026, learn how to select the best solution for your enterprise to reduce costs and accelerate query performance.

Learn how enterprise data fabric supports AI, analytics and governed data access across modern hybrid and multi-cloud environments.

Explore enterprise data platform architecture, use cases and selection criteria. See how Dremio supports analytics and AI at scale.

A table format is a specification that defines how to organize metadata about data files so that query engines can treat them as reliable, transactional tables. It sits between the query engine and the physical files.

Adding a column to a large production table used to require a plan. You'd write the migration script, schedule a maintenance window, kick off a backfill job that rewrote every data file to include the new column, and then wait. For a table with billions of rows on a busy lake, that wait could stretch […]

Something I keep hearing in conversations with customers and prospects right now is a version of the same frustration: we have executive buy-in, budget approved, models selected, and we still cannot ship anything meaningful. The AI initiative is stalled, and everyone is looking at the data team. Then there are the teams who are already […]

Your organisation has probably already had the "AI agents" conversation. Maybe it was at a board meeting, maybe it surfaced during quarterly planning, or maybe a team came to you with a proposal and a timeline. Either way, the conversation almost certainly centred on the AI: which model, which vendor, which use case. Very few […]

This guide explores what semantic layers are, their benefits and how they’re implemented within your enterprise data stack.

Dremio is not a traditional data warehouse. It is a unified platform that eliminates data silos through a federated query engine, secures your object storage with an Iceberg-based lakehouse, and accelerates insights with an Agentic AI layer.

Retail analytics decisions: markdown timing, campaign activation, inventory replenishment. They are supposed to be data-driven. But at most retailers, the data isn't ready when the decision needs to be made. Your merchandising team sends a request to data engineering on Monday. The report comes back Thursday. By then, the sell-through window has shifted, the promotion […]