The Dremio Blog

Unveiling Dremio Cloud on Azure, a Fast, Scalable and Secure Lakehouse Platform Introduction Today we introduce Dremio Cloud on Azure, newly landing on Azure in November 2023, in public preview. Dremio Cloud is a powerful lakehouse platform providing streamlined self-service, unparalleled SQL performance, centralized data governance and seamless lakehouse management. In this blog post, we […]

The Dremio Unified Analytics Platform brings your users closer to the data with lakehouse flexibility, scalability and performance at a fraction of the cost.  We're excited to show you some new things we've been working on to help you analyze your data faster and easier than ever, no matter where it's stored. We’ve launched AI-powered […]

This blog helps you learn about array functions in Dremio Cloud and Dremio Software v24.3+.

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 […]

This blog helps you learn about table-driven access policies in Dremio Cloud and Dremio Software v24.1+.

This blog helps you learn about tabular UDFs in Dremio Cloud and Dremio Software v24.1+.

We're always looking for ways to better handle and save money on our data. That's why the "data lakehouse" is becoming so popular. It offers a mix of the flexibility of data lakes and the ease of use and performance of data warehouses. The goal? Make data handling easier and cheaper. So, how do we […]

In the age of data-centric applications, storing, accessing, and managing data can significantly influence an organization's ability to derive value from a data lakehouse. At the heart of this conversation are data lakehouse table formats, which are metadata layers that allow tools to interact with data lake storage like a traditional database. But why do […]

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.

This blog helps you learn about array functions in Dremio Cloud and Dremio Software v24.2+.

Dremio Reflection Recommender simplifies the creation of Reflections to accelerate BI workloads by streamlining the design process.

Dremio Reflection Refresh automatically selects the fastest and most cost-effective refresh technique depending on table actions.

Dremio, the easy and open lakehouse, delivers analytics at the lowest cost.

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