Hyperacceleration enables Apache Spark™ to make the best use of underlying processing platforms with the help of a thin layer of software and advanced programmable hardware, such as FPGAs or SmartSSDs.
With Moore’s Law slowing, CPUs are increasingly the weak link in big data environments. FPGAs and GPUs can theoretically ease the burden, but this requires extensive programming.
Hyperacceleration is intelligent middleware that seamlessly integrates big data platforms, traditional CPUs, and new hardware accelerators. It consists of a compiler and run-time system that routes analytics computations to the advanced hardware for high-performance execution automatically.
Jobs run up to 10X faster without changing your Apache Spark code, increasing data science productivity and the ability to meet tight SLAs.
Incorporate fuller data sets and more data sources by eliminating the bottlenecks forcing you to make compromises.
Instead of scale-out and scale-up (adding or upgrading servers), optimize your existing Spark environment with hyperacceleration, lowering hardware and operating expenses.
Despite advances in Hadoop and Spark and a general shift from ETL to ELT, organizations still struggle to ingest data as fast as it’s created or to meet their SLAs. Hyperacceleration can dramatically boost your throughput.
From ingest to transformation to analytic processing, CPU roadblocks prevent real-time insights. Hyperacceleration can smooth the path.
Hyperacceleration can speed your queries by up to 10x.
Cybersecurity presents a dual concern—low-latency protection and advanced threat remediation. Get a performance boost to hit real-time correlation with hyperacceleration.
Enhance financial big data analytics through the Spark pipeline. There are broad applications of financial big data analytics, including modeling portfolio risk, credit risk, and fraud prevention
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Blog: How Adding Hyperacceleration Can Deliver the Business Value That Apache Spark™ Has Promised