2021•Unpublished venueRequires access

Big Data: A Survey Paper on MapReduce Big Data

sahana N. A, Mohammed Rafi

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Abstract

Abstract Hadoop MapReduce is processed for analysis large volume of data through multiple nodes in parallel. However, MapReduce has two function Map and Reduce, large data is stored through HDFS. Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over This survey intends to assist the database and open source communities in understanding various technical aspects of the MapReduce framework. In this survey, we characterize the MapReduce framework and discuss its inherent pros and cons. We then initiate the development strategies which were announced in the recent literature. We also consider the open points and summons which were raised on parallel data analysis with MapReduce

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What this paper is about

Abstract Hadoop MapReduce is processed for analysis large volume of data through multiple nodes in parallel. However, MapReduce has two function Map and Reduce, large data is stored through HDFS. Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over This survey intends to assist the database and open source communities in understanding various technical aspects of the MapReduce framework. In this survey, we characterize the MapReduce framework and discuss its inherent pros and cons. We then initiate the development strategies which were announced in the recent literature. We also consider the open points and summons which were raised on parallel data analysis with MapReduce

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Available abstract

Abstract Hadoop MapReduce is processed for analysis large volume of data through multiple nodes in parallel. However, MapReduce has two function Map and Reduce, large data is stored through HDFS. Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over This survey intends to assist the database and open source communities in understanding various technical aspects of the MapReduce framework. In this survey, we characterize the MapReduce framework and discuss its inherent pros and cons. We then initiate the development strategies which were announced in the recent literature. We also consider the open points and summons which were raised on parallel data analysis with MapReduce

Key concepts: Big data, Computer science, Data science, Volume (thermodynamics), Data-intensive computing, Open source, Function (biology), Database

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