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In today internet world, due to the current advent of new technologies, mobile devices, and communication media like social networking sites, the amount of data generated every year is growing at a very high rate.The growth of this generated data is beyond our imagination.It is impossible to store these huge data sets in RDMBSs like MySQL, as there are no specific formats of the data and that can be in either text or image formats.It requires the need of technologies which can easily manage and process huge volumes of structured and unstructured data in real-time and can protect data privacy and security.Big data technologies like MapReduce, Apache Flume, and Apache Spark can capture, store, and analyze this huge amount of data in very efficient and less costly manner.Spark and MapReduce programming frameworks provide an effective open-source solution for managing and analyzing the Big Data.MapReduce is a high-performance distributed Big Data programming framework.It processes the data in batch processing environment.On the other hand, Apache Spark is a scalable distributed inmemory data processing engine.It processes the data in both batch and real time environment.It uses Resilient Distributed Datasets (RDD) and Directed Acyclic Graph (DAG) for data processing.In this paper, a review on Hadoop MapReduce and Apache Spark have been made by comparing them on various parameters like performance, streaming, fault tolerance, storage, language support, and reliability.
Vijay et al. (Tue,) studied this question.