In-memory computing is software that allows data to be stored in random access memory (RAM), across a cluster of computers, and processed in parallel. Accessing data stored in a centralized database in RAM across multiple computers is approximately 5,000 times faster than accessing data from traditional disk drives. In-memory computing stores data in a distributed fashion, dividing an entire operational dataset into individual computers’ memory, each storing a portion of the overall dataset. Once data is partitioned, parallel distributed processing allows access requests to be fulfilled in near real time.
In-memory computing requires less hardware to support high-performance throughput. This enables data center consolidation and reduces capital costs as well as operational and infrastructure overhead. In-memory computing helps large businesses quickly detect patterns and analyze massive data volumes in real time. Other advantages of in-memory computing include: the ability to access terabytes of data instantly, enabling real-time reporting and visually rich interactive dashboards; the ability to cache countless amounts of data constantly, improving search response times; the ability to store session data, improving website performance; and the ability to process complex data-intensive events.
The in-memory computing technology developed by SAP, called High-Speed Analytical Appliance (HANA), uses a technique called sophisticated data compression to store data in RAM. The performance of SAP HANA is 10,000 times faster than standard disks, allowing companies to analyze data in seconds instead of hours. SUSE Linux Enterprise Server for SAP Applications is a reference development platform for SAP HANA that also provides high availability, automated failover and rapid recovery.