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A COMPARATIVE STUDY OF HADOOP AND SPARK FRAMEWORKS

BROWSE_DETAIL_CREATION_DATE: 23-08-2016

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BROWSE_DETAIL_TYPE: Thesis

BROWSE_DETAIL_SUB_TYPE: Masters

BROWSE_DETAIL_PUBLISH_STATE: Unpublished

BROWSE_DETAIL_FORMAT: PDF Document

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_SUBJECTS: TECHNOLOGY,

BROWSE_DETAIL_CREATORS: Ali, Arsan Mohammed Ali (Author),

BROWSE_DETAIL_CONTRIBUTERS: Yazıcı, Ali (Advisor),

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Big Data, MapReduce, Hadoop, Spark


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In this thesis, Apache Hadoop and Apache Spark are compared with respect to performance, resource usage, and speedups of four different Big Data workloads. The comparison is done by tuning the parameters with various combinations for the optimum performance of each framework. The evaluations show that, Spark outperforms Hadoop for WordCount, and Sort workloads. However, Hadoop outperforms Spark in Naive-Bayes workload, and there is no a remarkable difference in the performances for Grep workload.


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