The Dremio Blog

We have news. The Gartner® Market Guide for Agentic Analytics, published February 9, 2026, maps the platforms shaping an emerging category: software that applies AI agents across the data-to-insight workflow. Among approximately 37 vendors listed, Dremio is recognized for its Agentic Lakehouse Platform as a Representative Vendor. We believe this reflects the data foundation we […]

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 […]

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.

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.

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 […]

Compare 19 Databricks alternatives for data analytics, AI and lakehouse workloads, built to accelerate SQL and simplify operations.

The main differences come down to architecture, cost and AI readiness. Snowflake requires copying data into its warehouse and charges per compute credit consumed. Dremio queries data in place with Zero-ETL federation and includes an AI semantic layer, a built-in AI agent,and autonomous optimization. For a full breakdown, see the Dremio vs Snowflake comparison.

Accelerating the Agentic Lakehouse Today, we’re thrilled to announce that Dremio has agreed to join forces with SAP, pending regulatory approval. Together, we will be able to deliver one open platform where agents reason over all enterprise data, decide, and act. This acquisition will give us the scale and backing to accelerate our agentic vision, […]