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.

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.

In this blog, we’ve explored the technologies that enable the lakehouse paradigm, such as Minio for object storage, Apache Iceberg for ACID-compliant table formats, Nessie for catalog versioning, Apache Spark for distributed data processing, and Dremio for fast, SQL-based analytics.

Several of the world's largest data-driven organizations use Dremio to facilitate rapid analytics and achieve sub-second query response times directly on the lakehouse.  Reflections are one of the primary technologies in Dremio's query acceleration toolkit. Reflections are materializations that are aggregated, sorted, and partitioned in a variety of ways, and transparently accelerate queries irrespective of […]

I hope this guide has helped you on your journey in exploring whether Dremio is the right solution to eliminating data silos, reducing costs and overall improving your organizations data outcomes.

Dremio's latest version sets a new standard in the overall performance for lakehouse platforms. This release underscores Dremio's commitment to providing the most high performance Iceberg lakehouse platform, positioning it as the market's premier lakehouse analytics platform. Reflection Enhancements  A Reflection In Dremio, is an optimized relational cache that takes advantage of the platform's advanced […]

In today's data-driven world, organizations are increasingly dealing with diverse data environments, encompassing cloud, multi-cloud, on-premises, and hybrid. Efficiently managing and querying data across these varied landscapes can be challenging, particularly when it comes to access control and workload management. Dremio has introduced significant improvements in query federation capabilities, simplifying data access and ensuring robust […]

Dremio continues to innovate and enhance the capabilities of Data Lakehouse environments with its latest feature, Auto Ingest Pipelines for Iceberg tables. This cutting-edge functionality for both Dremio Enterprise Software and Dremio Cloud changes the way organizations handle data ingestion from Amazon S3 into Iceberg tables in  Lakehouse environments. What is Automatic Iceberg Data Ingestion? […]