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On the other hand, data movement is one of the primary causes of latency in real-time analytics. To avoid bottlenecks associated with the movement, it’s necessary to take the algorithms to machines where the data is stored. This makes it possible to speed up performance. The quality of your results and analytical conclusions is determined by algorithms; they specify the ideal computational procedures needed to process and calculate data.
The development of super-smart and faster algorithms and computers helps eliminate bottlenecks by improving the quality of analytics. Meanwhile, conducting training on a larger dataset in a shorter time ensures that the model is more accurate, which is the ultimate objective. Exploring additional modelling options becomes much easier. Using older algorithms is a sure-fire way of creating unwanted bottlenecks because they generally lack the capacity to cope with today’s big data challenges.