This post was originally published on Data Center Knowledge
Data infrastructure is undergoing its most significant transformation in decades. Generative AI and the shift toward heterogeneous accelerated computing environments that combine different hardware are reshaping the core requirements of a modern data stack. The ability to quickly and cost-effectively process complex datasets for AI and analytics has become a defining factor in operational efficiency and infrastructure ROI.
Historically, data processing performance has been determined by the sophistication of a query planner and the strength of an execution engine, with it assumed that the underlying hardware is the same across systems. Additionally, existing data processing benchmarks, such as TPC-DS and TPC-H, are designed to test the performance and efficiency of the system at the workload level.
Today’s data centers feature a wide range of accelerated computing hardware, including GPUs, TPUs, and FPGAs, with data processing performance and efficiency increasingly shaped by these hardware components as well. What was once a standardized infrastructure layer has evolved into a heterogeneous computing environment with distinct strengths and limitations.
Nearly every hardware vendor also claims that their hardware is best suited for data processing, citing specifications such as peak FLOPS, memory bandwidth, and tensor throughput. But these specs may not directly translate into the performance of real-world data
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