Exasol, an in-memory data warehouse, is separating its storage and computer capabilities and introducing it in its SaaS offering for public cloud and on-prem installations.
The system reworking would provide customers the flexibility and performance along with cost-optimization. Further, it will also provide cost advantages over various systems like Google’s BigQuery.
One analyst stated that its cost-based query optimizer was important to help users to obtain the cost-effective approach for performing query depending on the location and nature of various database objects. The same technical approach was launched in its SaaS solutions for the hosted public cloud and on-prem systems.
According to Golombek, the company CTO, “SaaS already offers multi-cluster elasticity through the separation of storage and computing. That same architecture will now be available for installations in your own cloud infrastructure — AWS, Google and Azure — and on-prem systems”.
Therefore, customers can keep the data ownership with them while taking advantage of SaaS solutions flexibility. They can also let them try new installations in the DBaaS prior to moving in preferred environments without changing the architecture.
In its new release, the company has also released the latest version of its cost-based optimizer to manage spending on complex queries with multiple tables in a single query. This will help them in performing easily data aggregations.
Matt Aslett, VP and research director said that Exasol has helped customers who faced a major performance issue with their data warehouse. It also includes those who thought they needed an analytic database with high-performance.
Therefore, according to him, “Developed as a parallel system based on a shared-nothing architecture, Exasol enables organizations to distribute queries across various nodes in a cluster using optimized and parallel algorithms to process data locally. This can provide linear scalability for more users and advanced analytics, including predictive analytics, by bringing AI/ML algorithms directly to the data”.