The Dremio Blog

Polaris is a next-generation metadata catalog, born from real-world needs, designed for interoperability, and open-sourced from day one. It’s built for the lakehouse era, and it’s rapidly gaining momentum as the new standard for how data is managed in open, multi-engine environments.

With Dremio, you’re not locked into a specific vendor’s ecosystem. You’re not waiting on data engineering teams to build yet another pipeline. You’re not struggling with inconsistent definitions across departments. Instead, you’re empowering your teams to move fast, explore freely, and build confidently, on a platform that was designed for interoperability from day one.

With traditional setups like Claude Desktop, that bridge is tightly coupled to a specific LLM. But with the Universal MCP Chat Client, you can swap out the brain, GPT, Claude, Gemini, Cohere, you name it, and still connect to the same tool ecosystem.

At Dremio, we have spent the last few years developing not only query execution improvements but also game-changing autonomous data optimization capabilities. Dremio is by far and away the fastest lakehouse.  The capabilities deliver 20x faster query performance compared to other platforms, without requiring any manual actions.

Companies today are under constant pressure to deliver faster insights, support advanced analytics, and enable AI-driven innovation. Many organizations chose  Amazon Redshift as their cloud data warehouse. However, as data volumes grow and workloads change, Redshift’s legacy warehouse architecture is not meeting their needs—driving many organizations to consider alternatives. Dremio’s intelligent lakehouse platform: a modern, […]

This guide will equip you with the expertise to easily build an AI-ready data product using Dremio and dbt.

By using properties, Puffin files, and REST catalog APIs wisely, you can build richer, more introspective data systems. Whether you're developing an internal data quality pipeline or a multi-tenant ML feature store, Iceberg offers clean integration points that let metadata travel with the data.

The traditional approach of caching table metadata and periodically refreshing has various drawbacks and limitations. With seamless metadata refresh, Dremio now provides users with an effortless experience to query the most up-to-date versions of their Iceberg tables without wrecking the performance of their queries. So now a user querying a shared table in Dremio Enterprise Catalog powered by Apache Polaris for example can see updates from an external Spark job immediately with no delay, and they never even have to think about it.

Confluent’s TableFlow and Apache Iceberg unlock a powerful synergy: the ability to stream data from Kafka into open, queryable tables with zero manual pipelines. With Dremio, you can instantly access and analyze this real-time data without having to move or copy it—accelerating insights, reducing ETL complexity, and embracing the power of open lakehouse architecture.

Incremental materializations allow you to build your data table piece by piece as new data comes in. By restricting your build operations to just this required data, you will not only greatly reduce the runtime of your data transformations, but also improve query performance and reduce compute costs.

That’s where Sampling comes in. And what if you want to give the user — or the LLM — reusable, structured prompt templates for common workflows? That’s where Prompts come in. In this final post of the series, we’ll explore: How sampling allows servers to request completions from LLMs How prompts enable reusable, guided AI interactions Best practices for both features Real-world use cases that combine everything we’ve covered so far

The future of the lakehouse depends on collaboration. Apache Polaris embodies the principles of openness, vendor neutrality, and enterprise readiness that modern data platforms demand. By aligning around Polaris, the data community can reduce integration friction, encourage ecosystem growth, and give organizations the freedom to innovate without fear of vendor lock-in.

Tools are executable functions that an LLM (or the user) can call via the MCP client. Unlike resources — which are passive data — tools are active operations.

One of MCP’s most powerful capabilities is its ability to expose resources to language models in a structured, secure, and controllable way.

At a recent customer panel moderated by Maeve Donovan, Senior Product Marketing Manager at Dremio, three of Dremio's largest customers came together with Tomer Shiran, Founder of Dremio, to share their experiences implementing Dremio's intelligent lakehouse platform. Antonio Abi Saad, Group Chief Data Officer at Sodexo, Karl Smolka, Associate Vice President - Data Platform & […]