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A Platform for Big Data Analytics on Distributed Scale-out Storage System

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dc.contributor.author Aye, Kyar Nyo
dc.contributor.author Thein, Thandar
dc.date.accessioned 2019-07-16T06:11:56Z
dc.date.available 2019-07-16T06:11:56Z
dc.date.issued 2015
dc.identifier.issn 2053-1389
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/913
dc.description.abstract Big data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. Hadoop-based platform emerges to deal with big data. In Hadoop NameNode is used to store metadata in a single system’s memory, which is a performance bottleneck for scale-out. Gluster file system has no performance bottlenecks related to metadata. To achieve massive performance, scalability and fault tolerance for big data analytics, a big data platform is proposed. The proposed big data platform consists of big data storage and big data processing. The Hadoop big data platform and the proposed big data platform are implemented on commodity Linux virtual machines clusters and performance evaluations are conducted. According to the evaluation analysis, the proposed big data platform provides better scalability, fault tolerance, and faster query response time than the Hadoop platform. en_US
dc.language.iso en en_US
dc.publisher International Journal of Big Data Intelligence (IJBDI) en_US
dc.relation.ispartofseries IJBDI;Vol-2, Issue-2
dc.subject big data en_US
dc.subject big data analytics en_US
dc.subject big data platform en_US
dc.subject Hadoop MapReduce en_US
dc.subject Gluster File System en_US
dc.subject Apache Pig en_US
dc.subject Apache Hive en_US
dc.subject Jaql en_US
dc.title A Platform for Big Data Analytics on Distributed Scale-out Storage System en_US
dc.type Article en_US


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