CPUs are general-purpose. They handle all processing pretty well. But technology has come a long way and some hardware is far superior for specific computations.
FPGAs and GPUs are programmable hardware that can provide compute efficiency. FPGAs in particular provide compelling advantages for data platforms like Apache Spark.
Hyperacceleration improves performance of data platforms like Apache Spark by automatically optimizing them with compute hardware. The flexible approach gives access to efficient hardware like Xilinx Alveo FPGAs with no impact on user Spark coding.
Bigstream Hyperacceleration has flexible approach, with emphasis on FPGAs
Bigstream delivers Hyperacceleration with FPGAs whether you run Spark clusters on premises or in the cloud.
The value of Bigstream is that it automatically provides the communication and translation between Spark code and the computing environment.
While other acceleration approaches force you to alter your code or switch platforms entirely, with Bigstream your Spark code remains 100% unchanged.
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, optimize your existing Spark environment with Bigstream, lowering hardware and operating expenses
