The Dremio Blog

Dremio offers three distinct integration points to the data in your lakehouse. This gives users the freedom to pick the interface, models, and tools that are right for them. Whether you're a business user, a seasoned data analyst, or a developer, we have an integration that will suit how you like to work. The built-in […]

Agentic AI is how leading financial institutions turn risk aggregation and regulatory reporting from a slow, manual burden into a real‑time, always on advantage, boosting accuracy, slashing costs, and accelerating insight. Dremio’s Agentic Lakehouse gives financial institutions the data foundation and AI agents they need to industrialize risk aggregation and regulatory reporting, with higher accuracy, […]

Why Dremio built a command-line tool designed to be introspected by machines. When GitHub launched gh in 2020, they framed the problem as context switching: developers losing flow by bouncing between terminal and browser. When Stripe shipped their CLI, the pain was webhook testing. When Fly.io built flyctl, the argument was philosophical: web apps aren't […]

This post walks through the four capabilities that make Dremio the easy button for building a unified, governed, AI-native data platform.

The data lakehouse takes a different approach. It deconstructs these components into modular, interchangeable layers, each built on open-source standards. This post walks through the Apache Software Foundation projects that form the core of the open lakehouse stack, what each one does, and how Dremio integrates them into a production-ready platform with built-in AI capabilities.

Working with JSON in an Iceberg lakehouse has always been a compromise: you either store JSON as VARCHAR strings and accept the performance hit every time a query needs to extract a field, or you flatten the JSON into a wide table of nullable columns and watch your schema bloat. Both work fine but have […]

Financial crime has become a trillion‑dollar problem, and the only sustainable way to fight it is with AI‑driven, real‑time analytics on complete, well‑governed data. Dremio’s Agentic Lakehouse platform is designed to give Financial Services organizations exactly what effective fraud and AML programs need: unified data, governed access, and sub‑second analytics across historical and streaming data. […]

The third annual Iceberg Summit is happening this week and it’s rapidly growing into one of the must attend data events for the year. Why? Well, Iceberg won the table format wars a couple years ago because companies wanted to avoid lock-in and they wanted interoperability. The Iceberg lakehouse also quietly became the default data […]

Governed Agentic Access gives agents a safe, fast, purpose-built interface to the platform. Access controls extend cleanly to agent workloads. MCP removes integration friction. AI SQL Functions bring unstructured data analysis into the same query layer. Autonomous Reflections ensure that governance overhead doesn't come at the cost of prohibitive latency.

For years, the promise of the open lakehouse was simple: store your data once, query it with any tool, and never get locked into a single vendor's ecosystem. Apache Iceberg made that promise real. It became the industry-standard table format because it worked, it was open, and it kept getting better. Iceberg version 3 (V3) […]

Apache Iceberg, the open and interoperable table format that has become the industry standard for the modern data lakehouse, adds meaningful new capabilities in Iceberg version 3 (V3). With the March release of Dremio Cloud, those capabilities are now available in all regions. The release brings multiple support for multiple areas: This post covers what's […]

The investment in the semantic layer pays off not just in agent accuracy but in the reliability of every downstream workflow that depends on agent output.

For data engineers building the foundation for agentic analytics, this open-standards approach also means less lock-in risk. The investment in modeling data as Iceberg tables is portable. The catalog is accessible to any Iceberg-compatible engine.

If agentic analytics is on your roadmap, or if you're already building AI applications that need to connect to enterprise data, it's worth auditing where your current platform sits across these three pillars. Most gaps show up fastest when agents start hitting data quality issues, permission errors, or ambiguous schema definitions that a human analyst would have talked their way around.

If you haven’t arrived at this conclusion, you will. If you’ve started transitioning some of your analytics to agents, you’ll know you’ll be here soon. Reporting is fundamentally different, and 100x better in the AI-era. I don’t say that to be provocative. I say it because of what we’ve changed internally at Dremio over the […]