The Dremio Blog

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.

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.

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, […]

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 & […]

Modern data teams are under pressure to deliver faster insights, support AI initiatives, and reduce architectural complexity. To meet these demands, more organizations are adopting active data architectures—frameworks that unify access, governance, and real-time analytics across hybrid environments. In the newly released Dresner 2025 Active Data Architecture Report, Dremio was ranked #1—recognized as a top […]

Enterprises today face increasing pressure to extract insights from data quickly while controlling spend. Yet, as data volumes explode across cloud and on-prem environments, traditional architectures often fall short—resulting in higher costs, rigid pipelines, and slower decision-making. The Dremio Intelligent Lakehouse Platform addresses these challenges by delivering faster insights and significant total cost of ownership […]

In this post, we’ll peel back the curtain on the inner workings of LLMs. We’ll explore the fundamental concepts that make these models tick: embeddings, vector spaces, and context windows. You’ll walk away with a clearer understanding of how LLMs “understand” language — and what their limits are.

Artificial Intelligence (AI) has become essential for modern enterprises, driving innovation across industries by transforming data into actionable insights. However, AI's success depends heavily on having consistent, high-quality data readily available for experimentation and model development. It is estimated that data scientists spend 80+% of their time on data acquisition and preparation, compared to model […]

This post kicks off our 10-part series exploring how AI evolved into LLMs, how to enhance their capabilities, and how the Model Context Protocol (MCP) is shaping the future of intelligent, modular agents.

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.

Business data needs are quickly evolving, and technology is adapting to keep pace. Cloud data warehouses now offer elastic storage and compute. Data lakes have evolved into lakehouses, combining lakes' flexibility with warehouses' reliability. Many organizations are utilizing a hybrid on-prem + cloud data storage strategy. Transformation tools have shifted from proprietary ETL platforms to open-source frameworks that enable software engineering practices on analytics. These technological advances are fundamentally changing how organizations work with data.

Traditional data management techniques increasingly struggle to keep pace with modern data's volume, variety, and velocity. The need to evolve legacy data management to enable AI-ready data has caused organizations to evaluate their data strategies. Two innovative approaches have gained prominence: Data Mesh and Data Fabric.

Modern organizations are increasingly reliant on data to drive innovation, optimize operations, and gain a competitive edge. However, extracting meaningful insights from the ever-growing volume of data presents a significant challenge. Despite substantial investments in data infrastructure and specialized teams, many organizations struggle to make their data readily accessible and actionable for decision-making. The traditional centralized approach to data management, while offering control and standardization, often leads to bottlenecks, delays, and frustrated data consumers. This, in turn, can hinder agility, stifle innovation, and ultimately impact the bottom line.

Organizations are constantly seeking ways to optimize data management and analytics. The Dremio and NetApp Hybrid Iceberg Lakehouse Reference Architecture brings together Dremio’s Unified Lakehouse Platform and NetApp’s advanced data storage solutions to create a high-performance, scalable, and cost-efficient data lakehouse platform. With this solution combining NetApp’s advanced storage technologies with Dremio’s high-performance lakehouse platform, […]