The Dremio Blog

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.

Dremio provides a different approach. Instead of replacing Snowflake entirely, you can layer Dremio on top of it, offloading the expensive, repetitive queries to Dremio's engine while keeping Snowflake for the workloads it handles best. Dremio's Autonomous Reflections, AI-powered analytics, and federated query engine reduce the compute Snowflake needs to process, often cutting the bill by 40-60% in the first month.

If your team manages both a warehouse and a lake, give Claude the context it needs to actually help you. Using a dedicated MCP server bridges the gap between powerful language models and your complex data architecture.

Today we're making it super simple to use Claude, Claude Cowork, and Claude Code with Dremio.  The new Claude connector for Dremio Cloud lets anyone instantly connect Anthropic's Claude to their lakehouse.  

The Data Crisis Crippling Financial Services Most financial institutions today face the same bottleneck: fragmented, slow, and costly data systems that cripple the adoption of AI. Analysts wait hours for risk reports. Data engineers spend most of their time tuning queries instead of innovating. And autonomous AI agents hit performance walls and data inconsistencies that […]

This tutorial shows you how to build a supply chain analytics pipeline on Dremio Cloud that unifies procurement, warehouse, and sensor data. You'll seed sample datasets, model them through Bronze, Silver, and Gold views, and use the AI Agent to evaluate supplier performance and inventory risk through natural language questions.

This tutorial shows you how to build a healthcare analytics pipeline on Dremio Cloud that unifies patient, claims, and prescription data in real time. You'll create sample datasets, model them into Bronze, Silver, and Gold views, and use the AI Agent to analyze readmission risk and cost patterns through natural language questions.