The Dremio Blog

Modern organizations face a common challenge: efficiently analyzing massive datasets stored in data lakes while maintaining performance, cost-effectiveness, and ease of use. The Dremio Architecture Guide provides a comprehensive look at how Dremio's innovative approach solves these challenges through its unified lakehouse platform. Let's explore the key architectural components that make Dremio a transformative solution for modern data analytics.

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.

Dremio’s integration with Unity Catalog and Hive/HDFS empowers organizations to harness the full potential of their hybrid data environments. By simplifying access, accelerating queries, and providing a robust platform for data curation, Dremio helps organizations build a unified, high-performance data architecture that supports faster, more informed business decisions.

In summary, Dremio’s platform bridges the cloud and on-prem data divide, providing a unified, high-performance solution for hybrid data analytics. By combining the strengths of Polaris and Hive/HDFS in a single environment, organizations can gain a deeper understanding of their data, drive operational efficiencies, and deliver real-time insights that support strategic growth.

The data landscape is evolving at an unprecedented pace, and organizations are constantly seeking ways to maximize the value of their data while maintaining flexibility and control. Dremio 25.2 rises to meet these needs by expanding its support for lakehouse catalogs and metastores across all deployment models: on-premise, cloud, and hybrid. This release makes Dremio […]

The beauty of Iceberg REST Spec is that it provides a stable interoperability interface for Iceberg clients. For Dremio users, this means that they can get access to Iceberg data where it lives rather than having to build and maintain complex ETL pipelines which increase governance challenges, data latency and operational overhead. To further our […]

Learn more about Apache Polaris by downloading a free early release copy of Apache Polaris: The Definitive Guide along with learning about Dremio's Enterprise Catalog powered by Apache Polaris. Analysts demand tools that offer fast, flexible, and scalable access to data. Interoperability between data platforms is crucial to enable seamless data exploration and querying, without […]

We’re excited to bring the Dremio Lakehouse Catalog for Apache Iceberg into Dremio Software!

With the introduction of full dark mode, Dremio is continuing its trend toward offering users more customization and control over their experience. Whether you prefer a light, bright workspace or a darker, more subdued environment, Dremio now provides the flexibility to match your personal workflow and preferences.

Dremio’s unique ability to support cross-environment queries and accelerate them with reflections enables businesses to leverage a true lakehouse architecture, where data can be stored in the most suitable environment — whether on-premises or in the cloud — and accessed seamlessly through Dremio.

For enterprises seeking a smarter approach to data management, the Dremio Hybrid Iceberg Lakehouse provides the tools and architecture needed to succeed—offering both cost savings and faster time to insight in today’s rapidly changing business landscape.

For organizations looking to modernize their data architecture, an Iceberg-based data lakehouse with Dremio provides a future-ready approach that ensures reliable, high-performance data management and analytics at scale.

By following this guide, you now have a local setup that allows you to experiment with Iceberg tables in a flexible and scalable way. Whether you're looking to build a data lakehouse, manage large analytics datasets, or explore the inner workings of Iceberg, this environment provides a solid foundation for further experimentation.

For enterprises seeking to unlock the full potential of AI, Dremio  provides the tools needed to deliver AI-ready data, enabling faster, more efficient AI development while ensuring governance, security, and compliance. With this powerful lakehouse solution, companies can future-proof their infrastructure and stay ahead in the rapidly evolving world of AI.

Catalog versioning with Nessie and code versioning with dbt both serve distinct but complementary purposes. While catalog versioning ensures the integrity and traceability of your data, code versioning ensures the collaborative, flexible development of the SQL code that transforms your data into actionable insights. Using both techniques in tandem provides a robust framework for managing data operations and handling inevitable changes in your data landscape.