The Dremio Blog

Is Query Performance Slowing Down Your AI and Analytics Initiatives? Slow analytics and AI workloads frustrate users and delay critical insights, draining productivity. If waiting for queries to load feels like the norm, you're not alone. But what if query performance could be accelerated—automatically, without requiring any specialized expertise or manual intervention? Enter Autonomous Reflections. […]

Companies of all sizes now use lakehouse architectures to power their analytics and AI workloads. Lakehouses give companies a single, trusted source of data for analytics and AI tools to access, and eliminate the need for data duplication and vendor lock-in. The catalog, or metastore, is an integral part of the lakehouse that enables tools […]

Simplifying Kubernetes Management for the Modern Data Team Enterprise customers leveraging Dremio on Kubernetes (K8s) have long valued the ability to scale engines according to their performance and cost requirements. However, until now, this capability required specialized Kubernetes expertise and manual configuration processes that added complexity to your data infrastructure management. Today, we're excited to […]

Organizations generate vast amounts of data, spanning multiple sources, tables, views, and scripts. Traditional keyword-based search methods often fall short, returning irrelevant results and making data discovery and exploration difficult and time consuming. With AI-Enabled Semantic Search, discovering relevant data to help answer questions and solve business problems becomes intuitive, simple, and quick. AI-Enabled Search […]

Unlocking Effortless Data Organization with Dremio’s Iceberg Clustering Organizations today face significant challenges optimizing their data lakes for performance while minimizing engineering overhead. That's why Dremio is excited to introduce Iceberg Clustering, a powerful capability that intelligently optimizes the data layout in your Apache Iceberg lakehouse.. With Iceberg Clustering, Dremio automatically reorganizes data within partitions, […]

In this tutorial, we’ll walk you through building a beginner-friendly MCP server that acts as a simple template for future projects. You don’t need to be an expert in AI or server development—we’ll explain each part as we go.

You're not alone if you're currently stretching SQL Server—or any OLTP database—beyond its intended purpose to keep up with analytics demand. This pain point is shared by countless organizations as data volumes grow, dashboards become more complex, and business expectations rise.

Unlike traditional databases, Iceberg doesn’t bundle storage, metadata, and catalog into a single system. Instead, it gives you flexibility—with the tradeoff that restoring from a backup requires understanding how those components fit together:

While the table spec and catalog spec laid the groundwork for interoperability and governance, it’s Table Services that will determine whether your Iceberg tables thrive or degrade in the real world. They’re the unseen engine room that keeps data performant, cost-effective, and reliable—especially at scale.

The Model Context Protocol is quietly reshaping how we build with language models — not by making the models smarter, but by making their environments smarter.

In conclusion, securing an Apache Iceberg lakehouse demands a holistic strategy that encompasses multiple layers of control. By implementing robust security measures at the object storage level, such as encryption and access restrictions, organizations can protect the raw data.

With Confluent Tableflow and Dremio, businesses can query real-time and historical data together in an open lakehouse architecture providing insights at the speed of operational data.

By embracing unified data products, organizations can move beyond vendor lock-in, streamline data access for BI and AI, and future-proof their data architectures. With Dremio’s platform, enterprises can build the foundation for a truly unified, high-performance data ecosystem that meets the needs of modern data consumers.

By leveraging the Dremio and dbt 's capabilities and adhering to these best practices, business units can build a strong CI/CD pipeline that improves both the quality and speed of data transformation projects.

The Apache Polaris roadmap lays out an ambitious vision for the project, balancing core functionality, governance, security, and interoperability while staying true to its open-source roots. As Polaris evolves, its flexibility, community-driven approach, and commitment to quality will ensure it meets the growing demands of modern data ecosystems.