Rapid Data Sorting Technique with Efficient and Dynamic Approach

In the data science world, massive amounts of data need to be processed efficiently as part of a high-volume of data processing. As the input data sets are highly disordered, we need to embed the appropriate algorithm to arrange the data in the required order for SQL queries to process the data quickly. Processing data in billions or trillions of rows has become common use cases. Robust data management strategies are required to handle increasing data volume. The main reason for data growth is use of IoT devices, ERP platforms, Social media apps, e-Commerce platforms, streaming data and AI / ML creates more data for data insights. A delay in few milliseconds for each input data sorting can make a difference of several minutes to hours when the system is processing larger data sets. The data sorting mechanisms are measured by their time complexity with the input element size benchmarking the processing time and resources consumed on a specific system. The data sorting performance can be improved by reducing the number of intensive operations (number of CPU cycles) and memory usage for each process when the data is sorted. “Rapid Data Sorting” provides much more efficiency to the program and thereby helps to improve the overall data processing speed. After extensive research and rigorous testing, the proposal below was formulated.