Abstract
More than a decade after Hadoop appeared on the scene as an open-source framework for big data analysis, much of its independent benefits has revolutionized fast processing of the data. Its development has triggered a number of improvisations for specific needs of data processing, depending on the nature of the processing techniques at different levels of computation. These upgrades have not only shot up the processing speed but also capable to counter intensive real-time computation requirements. This paper highlights the challenges posed by the basic Hadoop architecture and compares with the remedies as provided by Spark and Storm frameworks.
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