The Dremio Blog

With its capabilities in on-prem to cloud migration, data warehouse offload, data virtualization, upgrading data lakes and lakehouses, and building customer-facing analytics applications, Dremio provides the tools and functionalities to streamline operations and unlock the full potential of data assets.

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.

In today's data-driven world, organizations are constantly seeking innovative tools and technologies to harness the power of their data.

This article has been revised and updated from its original version published in 2022 to reflect the latest Dremio and Apache Iceberg capabilities. JSON is the lingua franca of APIs, event streams, and NoSQL databases, but its nested, schema-on-read nature makes it challenging for analytical queries. Converting JSON data to Apache Iceberg tables flattens nested […]

Apache Iceberg is a data lakehouse table format that has been taking the data world by storm with robust support from tools like Dremio, Fivetran, Airbyte, AWS, Snowflake, Tabular, Presto, Apache Flink, Apache Spark, Trino, and so many more. Although one of the tools most data professionals use is Apache Spark and many introductory tutorials […]

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.