The Dremio Blog

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.

This tutorial shows you how to build a financial analytics pipeline on Dremio Cloud in 30 minutes. You'll seed sample banking, market, and compliance data, model it into a medallion architecture, and use the AI Agent to detect transaction anomalies and assess account risk through natural language questions.

This tutorial walks you through building a complete Customer 360 view on Dremio Cloud, from signup to asking natural language questions about your customers. You'll seed sample data, model it through Bronze, Silver, and Gold views, enable AI-generated documentation, and use Dremio's built-in AI Agent to generate insights and charts. The entire process takes about 30 minutes with a free trial account.

Across Europe, organizations are modernizing analytics and AI while tightening requirements around data control, jurisdiction, and operational accountability. What customers are increasingly seeking is straightforward: the flexibility of open architectures, the speed needed for modern workloads, and the confidence that their service operations, support, and contracting align with European expectations. Today, I’m excited to share […]

Most enterprise AI projects fail at the same place. Not the model. Not the infrastructure. The data. Specifically: the AI doesn't understand the data well enough to act on it accurately, and it can't reach most of the data anyway. These two problems compound each other. A model that can only see 30% of your […]

Discover leading AI-powered data analytics solutions and see how they enhance insights, automation and enterprise decision-making.

Explore the key differences between Delta Lake and Iceberg, and learn how open data formats enable scalable, AI-ready lakehouse architectures.