The Dremio Blog

The concept of data marts has long been a pivotal strategy for organizations seeking to provide specialized access to critical data for their business units. Traditionally, data marts were seen as satellite databases, each carved out from the central data warehouse and designed to serve specific departments or teams. These data marts played a vital […]

The Databricks platform is widely used for extract, transform, and load (ETL), machine learning, and data science. When using Databricks, it's essential to save your data in a format compatible with the Databricks File System (DBFS). This ensures that either the Databricks Spark or Databricks Photon engines can access it. Delta Lake is designed for […]

Editor’s note, September 2026. This post was published in September 2023 and some product details have changed since. Nessie is still available and self-deployable under the Apache-2.0 licence, and it remains the clearest implementation of catalog-level branching. It is not an Apache Software Foundation project, and its development has slowed considerably. For how the current […]

This article has been revised and updated from its original version published in 2022 to reflect the latest developments in all three table formats. The three major open table formats (Apache Iceberg, Delta Lake, and Apache Hudi) each solve the "open lakehouse" problem differently at the architectural level. While the high-level comparison covers features and ecosystem support, […]

Using Airbyte, you can easily ingest data from countless possible data sources into your S3-based data lake, and then directly into Apache Iceberg format. The Apache Iceberg tables are easily readable directly from your data lake using the Dremio data lakehouse platform, which allows for self-service data curation and delivery of that data for ad hoc analytics, BI dashboards, analytics applications, and more.

In the last decade, a lot of innovation in the data space took place to address the need for more storage, more compute, hybrid environments, data management automation, self-service analytics, and lower TCO to operate business at scale.

Dremio Arctic bring new features to Dremio Cloud, including Apache Iceberg table optimization and Data as Code.

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.

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 […]