The Dremio Blog

By implementing these data quality checks, you can trust the accuracy of your data and make reliable decisions based on high-quality information. Data quality is an ongoing process, and these techniques serve as essential tools to ensure the integrity and usefulness of your datasets.

This blog presents how to get started with Flink SQL and Apache Iceberg for streaming analytical workloads.

This tutorial should show you how to work with Flink and Iceberg with a Nessie catalog to configure for your needs.

This month, we’re excited to announce exciting new features to help you enrich, accelerate, and secure your data in Dremio Arctic!

In this blog, learn how Dremio helps end users analyze massive datasets in their semantic layer without sacrificing speed or ease of use.

Editor’s note, September 2026. This post was published in May 2023 and some product details have changed since. Dremio Arctic is now Dremio’s Open Catalog, which is built on Apache Polaris rather than Nessie. The screenshots and product names below reflect the interface as it was at the time of writing. For how the current […]

Editor’s note, September 2026. This post was published in May 2023 and some product details have changed since. Dremio Arctic is now Dremio’s Open Catalog, which is built on Apache Polaris rather than Nessie. The screenshots and product names below reflect the interface as it was at the time of writing. Nessie is still available […]

Generative AI can be a powerful tool for data engineers, but it must be used responsibly and effectively.

This blog discusses how to streamline data quality and do audits for your Apache Iceberg tables using write-audit-publish and branching

In this blog we introduce a new CLI tool that allows us to easily and quickly migrate Apache Iceberg tables from one catalog to another without data copy.

Modernizing Hadoop infrastructure is essential in today's data-driven landscape. Learn how to boost performance through proven Hadoop modernization strategies.

The top 5 trends in data engineering are explored in this article

Apache Iceberg has a wide Python footprint to allow you to do the work you need to do. Whether you use pySpark, ODBC/Arrow to send SQL to engines like Dremio or use pyIceberg to do local scans of your table with DuckDB, Iceberg has a lot to offer data practitioners who love writing Python.

In this article, we will discuss how you can use technologies like Dremio and DuckDB in conjunction to create a low-cost, high-performance data lakehouse environment that is accessible to all your users.

Apache Iceberg, with its ever-expanding features, ecosystem, and support, is quickly becoming the standard format data lakehouse table. When adopting Apache Iceberg there are several paths of migration no matter where your data architecture currently resides.