The Dremio Blog

Dremio's Data and Analytics Predictions for 2024: data lakehouse adoption, the rise of Apache Iceberg, DataOps, Data Mesh and Generative AI

The demand for insightful and high-performance dashboards has never been greater. As organizations accumulate vast amounts of data, the challenge lies in visualizing this data efficiently, especially when dealing with large datasets. In this article, we will delve into the realm of BI dashboards, exploring the hurdles that hinder their performance for sizable datasets. Traditionally, […]

This month, we’re excited to announce automated table cleanup!

Dremio has released a new version of the Dremio vectorized Parquet reader that will improve query performance on Parquet datasets encoded with the Parquet V2 encodings by up to 75%

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 […]

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 […]

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.