The Dremio Blog

By using Dremio as the data gateway, organizations improve security, reduce complexity, and give their agents the reliable, performant access they need—without reinventing the data stack. This frees developers to focus less on credentials, connectors, and workarounds, and more on building the intelligent workflows that drive business impact.

A promise of self-service data platforms, such as the Data Lakehouse, is to democratise data. The idea is that they empower business users (BUs), those with little or no technical expertise, to access, prep, and analyse data for themselves. With the right platform and tools your subject matter experts can take work away from your […]

Dremio simplifies all of it. By bringing together query federation, an integrated Iceberg catalog, a built-in semantic layer, autonomous performance tuning, and flexible deployment options, Dremio makes it easier to build and run a lakehouse without stitching together multiple tools.

The MCP Server is a powerful alternative to the command line or UI for interacting with Dremio. But can only data analysts benefit from this transformative technology?

With the release of Apache Polaris 1.0, the data ecosystem takes a meaningful step forward in establishing a truly open, interoperable, and production-ready metadata catalog for Apache Iceberg. Polaris brings together the reliability enterprises expect with the openness developers and data teams need to innovate freely.

By following the steps in this guide, you now have a fully functional Iceberg and Polaris environment running locally. You have seen how to spin up the services, initialize the catalog, configure Spark, and work with Iceberg tables. Most importantly, you have set up a pattern that closely mirrors what modern data platforms are doing in production today.

Seamless result cache for Iceberg was enabled for all Dremio Cloud organizations in May 2025.  Since then, our telemetry has told us between 10% to 50% of a single project’s queries have been accelerated by result cache.  That’s a huge cost saving on executors.  Looking forward, Dremio is doing research on how to bring its reflection matching query re-write capabilities to the result cache.  For example, once a user generates a result cache entry, it should be possible to trim, filter, sort and roll up from this result cache.  Limiting the search space and efficient matching through hashes will be key features to make matching on result cache possible.  Stay tuned for more!

Back in April of this year Dremio debuted its own MCP server, giving the LLM of your choice intelligent access to Dremio’s powerful lakehouse platform. With the Dremio MCP Server the LLM knows how to interact with Dremio; facilitating authentication, executing requests against the Dremio environment, and returning results to the LLM. The intention is […]

The Polaris benchmarking framework provides a robust mechanism to validate performance, scalability, and reliability of Polaris deployments. By simulating real-world workloads, it enables administrators to identify bottlenecks, verify configurations, and ensure compliance with service-level objectives (SLOs). The framework’s flexibility allows for the creation of arbitrarily complex datasets, making it an essential tool for both development and production environments.

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.