Dremio Embraces Data Lakehouse with Cloud Launch
I previously explained how the data lakehouse is one of two primary approaches being adopted to deliver what I have called a hydroanalytic data platform. Hydroanalytics involves the combination of data warehouse and data lake functionality to enable and accelerate analysis of data in cloud storage services. The term data lakehouse has been rapidly adopted by several vendors in recent years to describe an environment in which data warehousing functionality is integrated into the data lake environment, rather than coexisting alongside. One of the vendors that has embraced the data lakehouse concept and terminology is Dremio, which recently launched the general availability of its Dremio Cloud data lakehouse platform.
Dremio was founded in 2015 to build a business around the Apache Arrow in-memory columnar data format, which was developed to enable high-performance analysis of large volumes of data. The company built a SQL query engine, now known as Dremio Sonar, to accelerate the use of standard business intelligence (BI) and visualization software to perform ad hoc analytics and dashboard-based analysis of data in data lake environments. Dremio also recently announced the preview release of Dremio Arctic, a metadata and data management service for the Apache Iceberg table format.
The combination of Dremio Sonar and Dremio Arctic is positioned as a data lakehouse platform. It is available via the Dremio Cloud managed-service offering or as self-managed software to run on a customer’s preferred cloud or data center environment. The core functionality of Dremio Cloud is free to use, with the company only charging for the Enterprise Edition, which includes security and data-masking capabilities, as well as commercial support.
Dremio customers include the likes of Airbus, Bose, Goldman Sachs, Hertz, HSBC, Prudential, Rakuten, and Samsung. The company has also attracted the interest of investors and has raised more than $400 million in funding from the likes of Adams Street Partners, Cisco Investments. Insight Partners, Lightspeed Venture Partners, Norwest Venture Partners, and Sapphire Ventures, with the latest $160 million Series E round valuing the company at $2 billion.
The data lake was first described in 2010 by James Dixon of Pentaho to refer to a single environment where large volumes of data could be stored and processed to be analyzed by multiple users for multiple purposes. Initially based on Apache Hadoop, data lakes are today increasingly based on cloud object storage services. More than one-half (53%) of participants in Ventana Research’s Analytics and Data Benchmark Research currently use object stores in their analytics efforts and an additional 18% plan to do so within the next two years. Data lakes have become an important part of the analytics data estate.
