Analytics Database Provider Exasol Launches DBaaS on AWS

In-memory analytics database vendor Exasol has introduced a new database-as-a-service capability on AWS.

London-based Exasol has been in business since 2000, with its online analytical processing (OLAP) database technology initially only for on-premises deployments.

The vendor first provided a cloud version in 2015, in a model where users deploy and manage the database themselves virtual private cloud. The new service, generally available now, now offers users a database as a service (DBaaS) model running on AWS.

Exasol is known for its in-memory capabilities, which support high-performance real-time analytical applications, noted Noel Yuhanna, analyst at Forrester Research. He added that in Forrester’s analysis, organizations value Exasol’s ease of use and reliability.

Yuhanna said he expects the new DBaaS model, released on Feb. 3, to help Exasol expand its market reach.

“The SaaS model will help organizations quickly scale up and down compute resources as we help reduce cost, optimize scale, and accelerate business use cases,” Yuhanna said. “Recently, we’ve seen stronger momentum with Exasol, particularly around the self-service and real-time analytics scenarios, so this new announcement will surely help keep the momentum going.”

Put the Exasol database in the cloud as DBaaS

At the core of the Exasol technology is what is known as an in-memory approach to loading data. With an in-memory database, the queried data resides in DRAM (DRACHMA), which aims to improve performance.

Recently, we’ve seen stronger momentum with Exasol, especially around self-service and real-time analytical scenarios, so this new announcement will surely help keep the momentum going.

Christmas YuhannaAnalyst, Forrester

Mathias Golombek, CTO of Exasol, explained that the vendor created its own proprietary in-memory algorithms to load and compress data. He noted that not all data resides in memory because the Exasol database accesses data from storage devices and loads data that needs to be analyzed into memory.

Exasol is commonly used with a number of business intelligence and data analysis tools, including Microsoft Power BI, Tableau, and Looker. Exasol is also increasingly being used in machine learning workloads with AWS Sagemaker.

Exasol Virtual Schemas Extend DBaaS Capabilities

Another fundamental feature of the Exasol database is a feature known as virtual schemas.

Golombek explained that with virtual schemas, users can extend Exasol to more easily import data from other sources, including relational databases, event data streaming, or APIs. He added that with virtual schematics, data is loaded into Exasol, benefiting from its in-memory capabilities when accessing the data.

Where virtual schema capabilities can be particularly useful is with cloud object services, such as Amazon S3, which are often used to enable a data lake. Exasol can support unstructured data in a data lake with a virtual schema approach, Golombek noted.

“So you can connect all kinds of external data sources to Exasol and run queries on the data,” he said.

Maria H. Underwood