The Dremio Blog

Explore how data unification works, discover leading, and learn how Dremio can help your enterprise drive better business insights.

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 […]

Apache Polaris is now a Top-Level Project at the Apache Software Foundation. For anyone building on Apache Iceberg, this is one of the most important catalog milestones since the REST Catalog spec itself.

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.

Data engineering teams are often defined by the gap between a business question and a verified answer. Requests languish in backlogs while engineering teams struggle with ETL pipelines and the technical debt of siloed warehouses. When reports finally arrive, the data is frequently stale, leading to debating the accuracy of the numbers rather than the […]

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

The data lakehouse market has moved past the “is this real?” phase. According to Forrester’s The Data Lakehouses Landscape, Q1 2026, lakehouses are now the default architectural choice for modern analytics and AI workloads, driven by the need to simplify data architectures, control costs, and support modern analytics and AI workloads. For technology leaders navigating […]

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.

Agentic analytics isn’t a trend. It’s the next phase of how organizations work with data. AI agents need access, speed, and context, across every system your business relies on.

Explore the factors that impact the average cost of a data warehouse, learn pricing models, and discover strategies to optimize your annual spend.

Explore the top data lakehouse solutions for 2026 and discover why Dremio is the best option for users who need cost-efficient, enterprise-grade performance.

Taken together, these changes show a project that is tightening its foundations while expanding its scope. Polaris 1.3.0 improves visibility through metrics, strengthens governance through externalized policy, and broadens catalog coverage through generic tables.

Last week I published a blog about some of the risks to consider when implementing Agentic AI in your workflows or organisation. But what about the other side of that coin, the benefits this revolutionary technology can bring about? Read on to learn several of the ways that Agentic AI is transforming how we work […]

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.