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SAP's Business Apps Harness In-Memory Computing

SAP is on a mission to harness what it calls the "transformative power of in-memory computing" with its release of SAP High-Performance Analytic Appliance (SAP HANA) software. This first version of SAP HANA sits alongside a joint announcement that sees the company also introduce SAP BusinessObjects Strategic Workforce Planning, an in-memory application based on SAP HANA.

With these new products, SAP is clearly hedging its bets on the traditionally understood "powerhouse" elements of in-memory data processing —i.e. the ability to leverage advances in main memory, tapping into the latest processor technology and speed advancements and the latest application intelligence. Combining these factors to bring analytics into a new category of applications, SAP is aiming to produce apps capable of processing large quantities of data with a calculation engine that enables application processing to be performed directly in-memory.

"SAP HANA and the first applications built on it are already showing customers the speed of in-memory computing, as well as the latency they have in the layers of their current IT systems," said Vishal Sikka, executive board member for the technology and innovation platform area at SAP AG. "SAP HANA provides a foundation on which a new generation of applications can be built, enabling customers to analyze large quantities of data from virtually any source in real time. With the integrative approach of SAP HANA and the supporting programming paradigm, SAP is simplifying existing computing layers and allowing applications to directly benefit from hardware improvements for the first time."

SAP claims that its latest releases for in-memory computing allows coding managers to simulate scenarios in real time, analyze complex relationships quickly and see how proposed organizational changes will impact the business. Managers and human resources executives could (for example) simulate how their workforce would need to grow and change using predictive modeling. This allows them to make intelligent, data-based decisions about allocating or adjusting staff, for example, as a result of an acquisition or to enter a new market.

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