The Dremio Blog

The future of AI-driven analytics isn’t about choosing between SQL and semantics, it’s about combining them. Dremio’s MCP server provides the best of both worlds, enabling agents to query, discover, and interpret enterprise data with confidence.

AI will only be as good as the data foundation it stands on. With Dremio’s AI-enabled semantic layer, organizations can unlock the promise of agentic analytics while maintaining governance, consistency, and performance.

The future of analytics isn’t about replacing people with AI, it’s about augmenting decision-making with intelligent, governed access to data. Dremio’s MCP server opens the lakehouse to this new generation of agents, ensuring that insights are not only fast but also accurate, secure, and aligned with business definitions.

Every year, the Gartner® Hype Cycle™ for Data Management helps us understand which technologies are generating buzz and which are delivering real business impact. In the 2024 report, one placement caught my attention: the data lakehouse has shifted from the Peak of Inflated Expectations into the Trough of Disillusionment. At first glance, this might sound […]

Apache Iceberg 1.10.0 represents a turning point in the evolution of the open lakehouse. With the general availability of format-version 3, Iceberg now offers a more complete solution for organizations seeking the flexibility of data lakes combined with the reliability of data warehouses. Features like binary deletion vectors, default column values, and row-level lineage aren’t just incremental improvements, they redefine what’s possible in managing massive, ever-changing datasets.

By standardizing the interaction between hosts, clients, and servers, MCP unlocks true modularity. You can swap models without breaking workflows, mix and match servers for analytics, email, or storage, and grow your AI capabilities incrementally. The Dremio + SendGrid example shows how easily analytics and action can come together, transforming what used to be manual, multi-step processes into fully automated workflows.

For organizations exploring agentic AI, this translates into a critical edge: AI agents can generate dynamic, ad-hoc questions and still receive sub-second, business-ready answers. With reflections, the performance layer is no longer a bottleneck, it becomes an enabler of intelligent, real-time decision-making.

Dremio’s MCP server, integrated semantic layer and autonomous reflections deliver this combination. They turn natural‑language intent into secure, performant and semantically rich analytics, enabling agents to act not just as chatbots but as trustworthy decision‑makers.

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 […]

Support for Hive Metastore federation and modular catalog integration makes Polaris more adaptable to real-world data environments. Enhancements to external authentication and Helm-based deployment reduce friction for teams operating in secure, regulated environments. And with expanded support for S3-compatible storage, the catalog can now accompany your lakehouse architecture into hybrid and edge deployments without compromise.

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 […]