The Dremio Blog

If you haven’t arrived at this conclusion, you will. If you’ve started transitioning some of your analytics to agents, you’ll know you’ll be here soon. Reporting is fundamentally different, and 100x better in the AI-era. I don’t say that to be provocative. I say it because of what we’ve changed internally at Dremio over the […]

Personality typing is a time-honoured tradition in tech. You've taken the Myers-Briggs, argued about whether you're really an INTJ, and been sorted into a primary-coloured group that allegedly encapsulates your work persona. But the test nobody was brave enough to publish is the one that actually matters to your daily work: which Dremio feature matches […]

Ask a typical data engineering team what percentage of their company's data they can actually query. The honest answer will be low, probably around 20%. The rest sits in object storage as PDFs, email threads, customer feedback forms, call transcripts, maintenance logs, and scanned documents. It's all there. It's all stored. And for the purposes […]

The data warehouse was never a product. It was an architectural intent. Here is why the lakehouse is its rightful successor, built on open standards.

Most companies process terabytes of JSON daily, yet querying it often requires brittle pre-processing pipelines and rigid data contracts. This has analysts and data engineers wasting hours defining explicit schemas just to run a simple aggregation. Dremio eliminates this friction by allowing you to query JSON directly in the lakehouse with complete schema-on-read flexibility. Without […]

Every enterprise has a data problem hiding in plain sight. Not the kind that shows up in board decks about cloud migration or AI strategy. The quieter kind: petabytes of files sitting in NetApp ONTAP systems — financial records, engineering documents, customer data, sensor logs — that power daily operations but stay invisible to every […]

Agentic AI is Front and Center for Financial Services Strategy Agentic AI is now at the center of competitive strategy in financial services, moving from pilots to production at scale. In this landscape, Dremio’s lakehouse-first approach gives banks, insurers, and wealth managers the data foundation they need to operationalize AI quickly and safely. AI Trends […]

For decades, the traditional data experience has been defined by friction, with business teams frequently required to wait. Waiting for SQL experts to draft queries, waiting for ETL pipelines to refresh, and waiting for static dashboards to render. This reactive model has gone past being just a bottleneck and now represents an existential risk for […]

Runtime filters in Dremio are an opportunistic, runtime‑only optimization: they do not replace good data modeling, partitioning, or reflections, but they stack on top of those fundamentals to remove work that is provably useless for a specific query run.

Learn how to build a customer 360 dashboard that unifies customer data and see how Dremio powers scalable, AI-ready analytics for enterprises.

Explore the nine best agentic analytics tools for data analysis in 2026, and learn why Dremio is the top solution for enterprise users.

Discover how analytics AI agents drive faster decisions when powered by a governed, scalable lakehouse foundation built for enterprise data.

The Apache Iceberg community recently finalised a new File Format API, scheduled for the upcoming 1.11.0 release. It is a strategic architectural shift that decouples the object model from the physical storage layout. The aim? To make file formats engine-agnostic, so Apache Iceberg can integrate with new formats without rewriting the core engine logic every […]

How data architects can close the gap between the AI mandate and the infrastructure that actually delivers it If you are a data architect right now, you are likely operating under some version of the same executive mandate: implement AI, and do it fast. The pressure is real. So is the gap between what leadership […]

Current Architecture (Conceptual) The current Dremio Software architecture often uses a fixed pool of executor engines. While this provides stability for baseline workloads, it struggles to handle predictable spikes in demand, leading to performance bottlenecks during peak periods, and overallocation of engines during quieter periods. Overallocation can have an impact on Cloud costs. This document’s […]