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May 30, 2023

Exasol Reimagines In-Memory Analytics with Major Database Update

Exasol has announced updates and enhancements to its in-memory analytics database. The company says the new release underscores its commitment to delivering a solution for its customers that does not require trade-offs between cost, efficiency, and flexibility.

These updates seek to meet the elasticity demands of enterprise data analytics, the company said in a blog post, adding: “Thanks to the introduction of our multi-cluster functionality, combined with the decoupling of storage and compute, Exasol users can seamlessly allocate additional resources to an active database in order to address their business requirements.”

The architecture of the Exasol Database has changed to decouple storage and compute. Previously, each database ran on a single cluster composed of multiple nodes, with each node’s local storage storing its segment of the data. Exasol has now transitioned to using object storage for persistent data storage, a change it has anticipated due to its embrace of the cloud model.

“When you have large OLTP systems generating massive amounts of data, to try to do anything innovative, companies are looking elsewhere,” Solongo Erdenkhuyag, Exasol global head of customer success told Datanami back in February. “This is where the cloud story comes into play. So you need a much more composable systems architecture. You want the best of breed.”

Though object storage exhibits higher latency compared to locally attached disks, the company says it is heavily optimized to scale with the amount of disk I/O requests and is a more cost-effective option than local storage. To address this latency issue, Exasol says it utilizes high performance ephemeral SSD storage in compute instances to cache data that demands low latency, including data used by Exasol server processes.

Additionally, the separation of storage allows for operation of the Exasol Database using multiple independent clusters accessing the same data, with one cluster serving the role of the main cluster that houses the Exasol Transaction Management System. The TMS ensures ACID compliance and enables various operations on any of the clusters while maintaining database consistency, the company says.

Exasol has also debuted a new SQL command to allow the migration of idle user sessions between sub-clusters, which it says allows for completely transparent setup change for the user.

The new offering is currently available for AWS and on-premises installations, with platform-specific storage implementations and optimizations for Google GCP and Microsoft Azure scheduled for release in the coming months, Exasol says.

Other new enhancements will be progressively delivered in the coming months, Exasol anticipates, including extended timestamp datatype, improved compile times, improvements for high concurrency situations, an improved optimizer for complex joins, zone maps for memory efficiency, and snapshot backups.

Exasol’s new object storage-oriented architecture. (Source: Exasol)

Exasol also announced an Accelerator Program for those interested in trying Exasol in their own tech stack with their own data. The company says this Accelerator Program will allow users to immediately access a complimentary SaaS trial or participate in a proof-of-concept with three months and five terabytes for free.

Founded in 2000, Exasol specializes in data loading and processing at very-large-scale concurrency and has found success by developing capabilities targeting specific workloads and use cases that require an in-memory database that can be deployed anywhere. Exasol’s three main deployment models are: plugging it in as a speed layer into an existing OLAP setup, using it as a primary data warehouse scalable up to petabytes of data, or implementing it as a consolidation layer for multiple data warehouses.

“Exasol believes customers shouldn’t ever have to make compromises with their analytics databases, especially during these times of economic uncertainty and reduced IT budgets. This is why our offering allows users to see significant performance and efficiency gains, while working within their budgets and existing tech environments,” said Joerg Tewes, CEO of Exasol. “We have hundreds of global customers using Exasol with extremely complex data, at scale. From financial services and retail customers reducing queries from hours to seconds, to agriculture firms working with complicated models supporting DNA sequencing, our customers spend more time analyzing and optimizing with less time and headcount.”

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