Surviving and thriving in the era of today’s, and tomorrow’s, crushing volume of data created by the Internet of Things (IoT) demands faster, more efficient data mining solutions that move beyond traditional x86 compute architectures. Organizations prepared to gain answers to their complex business challenges from their IoT data—video, social media, sensors, etc.—will have a significant competitive advantage.

A recent article from EnterpriseTech describes how a retail store must be able to capture and process high volumes of data, both streaming video surveillance and historical, to gain the real-time insight needed to deliver relevant incentives or coupons to the customer while they are still in the store. This is just one example of how organizations are using data analytics to solve complex business problems. However, until now, these scenarios have been challenging, and real-time data analysis has been limited by the sequential nature of x86 processors and the inherent bottlenecks caused by the ETL process. Also, most organizations did not have the high-performance computing resources needed. The EnterpriseTech article also considers the importance of high-performance data analysis and the need for actionable insights in a matter of milliseconds for industries such as financial compliance or high frequency trading.

The purpose-built Ryft ONE opens up a new world of data analytics to organizations by using a heterogeneous, FPGA-accelerated data analytics engine that combines the performance needed to gain actionable intelligence with a user-friendly Linux front end. The Ryft ONE delivers real-time insights into any type of data by eliminating ETL bottlenecks and providing the HPC performance in a 1U footprint. This gives any organization the HPC resources needed to harness its IoT data and solve its complex business problems to get the answers it needs from its data—not just the answers it can get.

Be sure to check out our video demo, and contact us to see a live demonstration to learn how Ryft can streamline and accelerate your data analysis by 100X.

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