The Dremio Blog

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.

Incorporating the Polaris Data Catalog into your Data Lakehouse architecture offers a powerful way to enhance data management, improve performance, and streamline data governance. The combination of Polaris's robust metadata management and Iceberg's scalable, efficient table format makes it an ideal solution for organizations looking to optimize their data lakehouse environments.

The combination of dbt and Dremio creates a powerful, agile data transformation pipeline. With dbt’s ability to standardize and automate transformations, and Dremio’s unified data platform optimizing and accelerating queries, organizations can unlock the full potential of their data.

Tools like Dremio are critical for breaking down data silos and providing real-time access to valuable insights. By simplifying data integration and making it actionable, these capabilities empower teams to make data-driven decisions and collaborate more effectively, ultimately delivering superior customer experiences and driving growth.

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.

The hybrid data lakehouse represents a significant evolution in data architecture. It combines the strengths of cloud and on-premises environments to deliver a versatile, scalable, and efficient solution for modern data management. Throughout this article, we've explored the key features, benefits, and best practices for implementing a hybrid data lakehouse, highlighting Dremio's role as a central component of this architecture.

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.