The Dremio Blog

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? […]

As organizations continue to navigate the complexities of modern data environments, Dremio’s self-service capabilities offer a clear path forward, allowing businesses to unlock the full value of their data assets while maintaining control and governance. With Dremio, the future of self-service analytics is not just achievable—it’s within reach.

By implementing Dremio, you can transform your existing data lakes into efficient, high-performing, and easily manageable data lakehouses. Whether you aim to modernize your infrastructure, facilitate seamless migrations, or create a hybrid data environment, Dremio provides the tools and capabilities to achieve your business goals.

Data source connections significantly impact the efficiency of your analytics workflows. Whether you're performing complex statistical analyses, building predictive models, or creating dashboards, your connection type influences your speed to insight. There are four main connection types: JDBC, ODBC, REST, and Arrow Flight. Understanding each helps you optimize your data pipelines for more effective analytics.

We're excited to announce enhancements to data branching that make data engineering workflows safer and more flexible!

While both Dremio and Starburst Data offer  tools for data management and analytics, Dremio's superior performance, self service capabilities, user-friendly design, and robust DataOps functionality make it the clear winner. Dremio is the choice  for companies looking to drive business value and insight with a next generation lakehouse solution.