Analytics database vendor Exasol launches DBaaS on AWS
The OLAP database vendor now has a new cloud service model that provides users with a managed instance for data analysis, business intelligence and machine learning.
In-memory analytics database vendor Exasol introduced a new database-as-a-service capability on AWS.
Exasol, based in London, 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 in which users deploy and manage the database on their own virtual private cloud. The new service, generally available now, now provides users with 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, an analyst at Forrester Research. He added that in Forrester's analysis, organizations like Exasol's ease of use and reliability.
Yuhanna said he expects the new DBaaS model, released on Feb. 3, will help Exasol expand its market reach.
"The SaaS model will help organizations to quickly scale up and down compute resources that we help lower cost, optimize scale and accelerate business use cases," Yuhanna said. "Recently, we have seen a stronger momentum with Exasol, especially around self-service, real-time analytical scenarios, so this new announcement will surely help to continue the pace."
Bringing the Exasol database to the cloud as DBaaS
At the foundation of the Exasol technology is what is known as an in-memory approach to loading data. With an in-memory database, data that is being queried resides in dynamic random access memory (DRAM), which is intended to provide improved performance.
Noel YuhannaAnalyst, Forrester
Mathias Golombek, CTO of Exasol, explained that the vendor has created its own proprietary in-memory algorithms to load and compress data. He noted that not all data resides in memory, as the Exasol database accesses data coming from storage devices and loads the data that needs to be analyzed into memory.
Exasol is commonly used alongside a number of different business intelligence and data analytics tools, including Microsoft Power BI, Tableau and Looker. Exasol is also increasingly being used as part of machine learning workloads with AWS Sagemaker.
Exasol virtual schemas extend DBaaS capabilities
Another foundational capability of the Exasol database is a feature known as virtual schemas.
Golombek explained that with virtual schemas, users can extend Exasol to bring in data more easily from other sources, including relational databases, event data streaming or APIs. He added that with the virtual schemas the data is loaded in Exasol, benefiting from its in-memory capabilities as the data is being accessed.
Where the virtual schema capabilities can be particularly useful is with cloud object services, such as Amazon S3, that 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 into Exasol and run queries on the data," he said.