The Dremio Blog

The direction is clear: the open lakehouse is no longer about choosing between flexibility and performance, or between innovation and governance. With Arrow, Iceberg, and Polaris maturing side by side, and with Dremio leading the charge, the open lakehouse has become a complete, standards-driven foundation for modern analytics. For enterprises seeking both freedom and power, this is the moment to embrace it.

Apache Iceberg closed that gap by transforming collections of Parquet files into true tables, complete with ACID transactions, schema flexibility, and time travel capabilities. And with Apache Polaris sitting on top as a catalog, organizations finally have a way to manage all those Iceberg tables consistently, delivering centralized access, discovery, and governance across every tool in the stack.

Introduction Dremio can automatically take advantage of partitioning on parquet data sets (or derivatives such as Iceberg or Delta Lake). By understanding the dataset’s partitioning, Dremio can perform partition pruning, the process of excluding irrelevant partitions of data during the query optimisation phase, to boost query performance. (See Data Partition Pruning). Partition bucketing provides a […]

What makes Polaris especially exciting is the trajectory it’s on. Today, it is a powerful, open catalog for Iceberg tables. Tomorrow, it could serve as the central control plane for managing a full range of lakehouse assets, unifying governance, access, and interoperability across an increasingly complex data ecosystem.

Column nullability serves as a safeguard for reliable data systems. Apache Iceberg's capabilities in enforcing and evolving nullability rules are crucial for ensuring data quality. Understanding the null, along with the specifics of engine support, is essential for constructing dependable data systems.

Reflections are a query acceleration functionality unique to Dremio, that work by minimising data processing times and reducing computational workloads. Debuting in the early days of Dremio, Reflections accelerate data lake queries by creating optimised Apache Iceberg data structures from file-based datasets, delivering orders-of-magnitude performance improvements. However, the game-changing aspect of this technology was not […]

When combined with Dremio’s query acceleration, unified semantic layer, and zero-ETL data federation, Enterprise Catalog creates a truly self-managing data platform—one where optimization is just something that happens, not something you have to think about.

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 […]