Performance Analysis of Distributed Computing Frameworks for Big Data Analytics: Hadoop Vs Spark

Shwet Ketu, Pramod Kumar Mishra, Sonali Agarwal


In the last one decade, the tremendous growth in data emphasizes big data storage and management issues with the highest priorities. For providing better support to software developers for dealing with big data problems, new programming platforms are continuously developing and Hadoop MapReduce is a big game-changer followed by Spark which sets the world of big data on fire with its processing speed and comfortable APIs. Hadoop framework emerged as a leading tool based on the MapReduce programming model with a distributed file system. Spark is on the other hand, recently developed big data analysis and management framework used to explore unlimited underlying features of Big Data. In this research work, a comparative analysis of Hadoop MapReduce and Spark has been presented on the basis of working principle, performance, cost, ease of use, compatibility, data processing, failure tolerance, and security. Experimental analysis has been performed to observe the performance of Hadoop MapReduce and Spark for establishing their suitability under different constraints of the distributed computing environment.


Big data, parallel processing, distributed environments, distributed frameworks, Hadoop MapReduce, spark, big data analytics

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