The Dremio Blog

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.

Agentic AI offers several compelling benefits that are transforming how we work and solve problems. The key difference from traditional AI is agency, the agent’s ability to plan, use tools, take actions in sequence, and work toward goals rather than just responding to individual prompts. There are many upsides to embracing AI agents in your […]

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.

Success depends on three non-negotiable pillars: deep business context from a semantic layer, universal access through live data federation, and interactive speed delivered by autonomous performance features.

Dremio's power can be fully leveraged within the Python ecosystem thanks to these versatile community-driven libraries.

Dremio's approach reveals a clear vision for the future of the data stack: AI is not a bolt-on feature but a foundational capability woven throughout the entire data lakehouse platform.

Dremio’s Open Catalog delivers a free, interoperable, and flexible foundation for the modern data lakehouse. By embracing open standards and a "bring your own" philosophy for compute, storage, and AI, Dremio provides a platform that prioritizes user choice and cost control over vendor lock-in. This commitment to openness gives you the flexibility to select the best tools to achieve your data analytics goals.

Achieving elite Dremio performance is a blend of smart architecture, understanding the platform's autonomous capabilities, and applying targeted, tool-assisted tuning. By moving beyond the basics, you can create a truly interactive experience for your data consumers.

Dremio is the engine that finally transforms the lakehouse into a high-performance BI reality. By unifying namespaces, automating acceleration via Reflections, and utilizing the power of Apache Iceberg, it moves the organization away from the "data moving" era.

The Dremio performance story is a narrative of engineering excellence meeting operational simplicity. By combining the efficiency of Apache Arrow, the "magic" of Iceberg-backed Reflections, and the intelligence of an AI-driven optimizer, Dremio has built a system where performance is an automated byproduct of the architecture.