The Dremio Blog

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 […]

In today's data-driven world, 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 compromising performance. Dremio, with its open data architecture, excels in providing powerful query engines that allow businesses to gain analytic insights without physically moving data. […]

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.

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.

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.

By integrating GitHub Actions into your dbt and Dremio workflows, you’ve unlocked a powerful, automated CI/CD pipeline for managing and version-controlling your semantic layer.

By embracing the right orchestration tools, you can automate your data workflows, save time, reduce errors, and scale your data platform with ease. So, whether you're managing daily queries or orchestrating complex data pipelines, Airflow combined with Dremio is the way forward for efficient and reliable orchestration.

In this tutorial, we demonstrated how to set up Dremio, promote and format a dataset, create a complex query, and then use an Aggregate Reflection to optimize that query for better performance. With this approach, you can easily scale your data analytics workload while keeping query times low.

With Dremio and Apache Iceberg, managing partitioning and optimizing queries becomes far simpler and more effective. By leveraging Reflections, Incremental Reflections, and Live Reflections, you can maintain fresh data, reduce the complexity of partitioning strategies, and optimize for different query plans without sacrificing performance. Using Dremio’s flexible approach, you can balance keeping raw tables simple and ensuring that frequently run queries are fully optimized.

We'll delve into how querying Iceberg metadata tables in Dremio can provide invaluable insights for table auditing, ensuring data integrity and facilitating compliance.

By leveraging Dremio's unified analytics capabilities and Streamlit's simplicity in app development, we can overcome the challenges of data unification.