Excellent architectural features that are worth calling out, such as: Faster data access from partitioned memory grid Hot & cold optimized caching Ability to ingest real-time/historical data from diverse data sources such as Kafka, RDBMS systems & Object Stores with ease Java/Spark-based data processing capabilities. Event-based processing Multi-model storage Multiple language/framework support (Java/.Net/Rest/Spark/BI Tools/SQL) for data consumption Scalability and Data replication options Data Grid Fault Tolerance Out of the Security and ability to extend the same Multiple Dev-Ops Integration options Support for Hybrid Cloud The features mentioned above qualify it for a centralized Enterprise Data Integration Hub, especially for cases that require time/mission-critical real-time analytics.

It´s a platform out of the box that allows address end to end data use cases, including analytics and real-time data processing (both transactional and analytical)

We are using Gigaspaces XAP as our primary data storage solution for our entire online system. Since we adopted XAP in 2010 we have grown in customers, activity and system complexity many times over and our backend microservices powered by Gigaspaces has always been able to scale and handle every business demand we've thrown at it. Gigasoaces XAP delivers very low latency and high scalability across our 1400+ microservices. It allows our developers to focus on functionality and business instead of code optimizations and complex design solutions.