The Dremio Blog

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.

Explore how a universal semantic layer can unify your data sources, simplify analytics, and help you achieve better insights.

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.

Enabling Guided, Intelligent Lakehouse Construction and Management Overview Modern lakehouse platforms have historically required significant technical expertise to operate effectively. Connecting data sources, modeling clean layers, applying governance semantics, creating views, and troubleshooting query failures have been tasks reserved for skilled data engineers. The learning curve has been a meaningful adoption barrier, particularly for organizations […]

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.

Dremio is a commercial platform, and we’re straightforward about that. But the standards and projects that power it are genuinely open, and Dremio has been an active contributor to building them, not just consuming them. Apache Arrow, Apache Iceberg, and Apache Polaris all have Dremio fingerprints on their design, specification, and governance. That work matters […]

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.