The Dremio Blog

This blog introduces the newly added branching and tagging capabilities for Iceberg tables using Spark.

Everyone recognizes that data platforms are critical for making data-driven decisions in all functions of an enterprise. A flaw in the foundation of your data platform can have significant cost and lost revenue implications.

This blog presents the benefit of the ROLLBACK table feature in Apache Iceberg that can help with data incidents.

Learn how to connect Apache Iceberg tables to view in Tableau using Dremio.

Learn how to create Apache Iceberg tables with Nessie catalog and use Nessie to create and merge branches.

Answers to many of the most common questions about Apache Iceberg.

This tutorial provides a step-by-step guide to migrate an Apache Superset dashboard built on a cloud data warehouse to an open lakehouse.

This tutorial shows how Dremio Arctic supports experimentation and reproducibility of machine learning models in the data lakehouse using Apache Iceberg, Spark & Dremio.

This tutorial presents a notebook guide to quickly get started with Apache Iceberg & Nessie using PySpark

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

Why does adopting a data lakehouse table format like Apache Iceberg matter and how it solves issues like data drift, costs, and more.

A semantic layer is a business representation of corporate data for your end users.

This blog looks at the evolution of the Apache Arrow project over the years from the time it was co-created by Dremio.

Dremio and dbt Labs provide a modern data architecture that is built around three core concepts: Built for SQL, Open, and Data-as-Code.

This article provides an introductory course on the concepts and practices of Apache Iceberg tables for running scalable data lakehouses.