The Dremio Blog

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 […]

Personality typing is a time-honoured tradition in tech. You've taken the Myers-Briggs, argued about whether you're really an INTJ, and been sorted into a primary-coloured group that allegedly encapsulates your work persona. But the test nobody was brave enough to publish is the one that actually matters to your daily work: which Dremio feature matches […]

Ask a typical data engineering team what percentage of their company's data they can actually query. The honest answer will be low, probably around 20%. The rest sits in object storage as PDFs, email threads, customer feedback forms, call transcripts, maintenance logs, and scanned documents. It's all there. It's all stored. And for the purposes […]

The data warehouse was never a product. It was an architectural intent. Here is why the lakehouse is its rightful successor, built on open standards.

Most companies process terabytes of JSON daily, yet querying it often requires brittle pre-processing pipelines and rigid data contracts. This has analysts and data engineers wasting hours defining explicit schemas just to run a simple aggregation. Dremio eliminates this friction by allowing you to query JSON directly in the lakehouse with complete schema-on-read flexibility. Without […]