The Dremio Blog

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 […]

The safest data architecture is one where data doesn't move, policies are unified in a central catalog, and every query is authenticated, authorized, and encrypted. By abstracting the complexity of data access and enforcing fine-grained controls at the catalog level, Dremio secures the data foundation so your teams—and your AI agents—can explore insights freely.

Dremio's Agentic Lakehouse provides an alternative for the workloads that drive the highest Databricks spend: interactive analytics, BI dashboards, and ad-hoc queries. By offloading these queries to Dremio's engine with Autonomous Reflections, you eliminate the DBU consumption and the underlying cloud compute for 60-80% of your analytical workload. Meanwhile, Databricks stays in place for the heavy processing it does well: ETL pipelines, ML training, and Spark-based transformations.

Dremio provides an alternative: keep Redshift for the workloads that need it, but offload the repetitive, expensive dashboard and reporting queries to Dremio's engine. Dremio's Autonomous Reflections serve those queries from Apache Iceberg tables on your own S3 storage, bypassing Redshift compute entirely. The result is a 40-60% reduction in Redshift compute costs in the first month, without migrating a single table.