While many organizations have a huge amount of data available, the limitations of central processing units make it difficult to process it all (CPUs). On the other hand, artificial intelligence (AI) has made tremendous progress thanks to the use of graphics processing units (GPUs), which have greatly accelerated algorithmic performance and opened up new opportunities for algorithms. However, big data has yet to develop its own distinct computing infrastructure, as CPUs continue to be the foundation for data analytics despite their inefficiency. We are now seeing a similar "revolution" where data scientists are finding ways to run data analytics workloads on dedicated processors with the same efficiency as AI workloads run on GPUs, leading to new levels of insights at previously unattainable speeds. We've seen AI researchers deviate from the standard path to find GPUs. Now, big data researchers are charting their own course and pushing the limits of big data analytics.
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