Category Archives: XAP

GigaSpaces Releases XAP 9.5: Enhanced for Cassandra Big Data Store, .NET Framework

GigaSpaces Technologies has released XAP 9.5, a new version of its in-memory computing platform that enables a quick launch of high-performance real-time analytics systems for Big Data.

At the core of the latest release of the GigaSpaces platform is XAP 9.5’s enhanced integration with NoSQL datastores, such as Cassandra. Combining the Cassandra datastore with the GigaSpaces in-memory computing platform adds real-time processing and immediate consistency to the application stack, while also guaranteeing dynamic scalability and transactionality – all necessary elements for enterprises that need real-time analytics or processing of streaming Big Data.

In this combined architecture, XAP in-memory computing provide the real-time data processing engine that is interoperable with any language or application framework, while the Cassandra DB provides long-term storage of data for use in real-time analytics.

GigaSpaces benchmark done for the integration of XAP with Cassandra shows that this integration dramatically improves real-time performance for data retrieval operations. Putting the GigaSpaces in-memory data grid in front of the Cassandra Big Data solution resulted in performance of read that is up to 2000 times faster.

Up until XAP 9.5. this integration was only available for XAP Java users. XAP 9.5 further innovates by allowing .Net users to leverage the same built in Cassandra integration. This integration provides a seamless bi-directional translation between Cassandra’s columnar data model and the richer document and object oriented models available in XAP. This works for both Java & .NET XAP deployments allowing for .NET developers to speed up their Cassandra based big data applications.

“The GigaSpaces XAP Cassandra integration enables companies to enjoy both in-memory data grid capabilities and Big Data processing, easily and for any framework – Java or .NET,” says Uri Cohen, GigaSpaces VP of Product. “This enables companies to be more agile in meeting both current and future data processing challenges.”