The Dremio Blog

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.

Apache Polaris provides the open foundation, but enterprises running enterprise-wide, AI-driven platforms need more than a standards-compliant catalog service. They need enterprise governance, automation, and business context that AI systems can reason over. As original co-creator of Apache Polaris and one of the project’s largest contributors, Dremio uses Polaris as the foundation of Dremio’s Open […]

Pick one area. If your federated queries are taking too long, migrate the largest dataset to Iceberg and measure the difference. If your semantic layer is a mess of redundant definitions, consolidate into a Bronze-Silver-Gold structure. If you're paying for compute on queries that run identically every hour, enable Autonomous Reflections and let Dremio handle the tuning.

By unifying federation, a robust semantic layer, and autonomous performance tuning, Dremio transforms the lakehouse from a static repository into an active partner.

By unifying all data sources, embedding deep business logic, and using AI to automate its own creation, this modern semantic layer removes the barriers that once made conversational analytics a distant dream.

Building an agentic lakehouse with Dremio moves your organization beyond the "phone book" catalog of the past and into a future where the catalog is a dynamic knowledge base.

The true power of Dremio’s AI Semantic Layer is that it provides the "grounding" context for AI agents. Because the catalog contains rich metadata and defined relationships, the AI agent doesn't just see columns; it understands business concepts.

Data is no longer a static resource for human eyes; the Agentic Lakehouse is about building an ecosystem where AI can act on data as effectively as we do.

The Dremio Open Catalog is the vital bridge between open-source flexibility and enterprise-grade AI readiness. By moving from a "static mirror" of Iceberg files to a semantic, agent-ready architecture, Dremio allows organizations to finally realize the promise of a self-managing data lakehouse.

Dremio Catalog fundamentally changes the game for data lakehouse adoption. By providing a free, fully managed, and feature-rich service built on the open Apache Polaris project, Dremio removes the significant barriers to standing up a universal Iceberg catalog.

The true value of Dremio isn't just one feature, but its integrated, "all-in-one" platform. This approach contrasts sharply with the DIY method, which often results in a lakehouse that is perpetually a work-in-progress, stitched together with technical debt.

With a platform like the Dremio Agentic Lakehouse, you can leapfrog legacy architectures and move directly to a flexible, cost-effective, and AI-native data platform.

Dremio's integrated platform transforms the agentic analytics challenge from a complex systems integration problem into a straightforward data conversation. By unifying federation, autonomous performance, a business-centric semantic layer, and conversational interfaces, Dremio eliminates the friction that has historically kept valuable insights locked away in data silos.