100791226_Google_Cluster_and_MapReduce.docx

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Surname 1 Name Instructor Course Date Google Cluster and MapReduce Cluster computing contains nodes which perform similar tasks and are controlled and also scheduled by developed software. For instance, Google’s Web Search has an application that allows for dissimilar queries to be run on varied processors thus allows for a faster computation of the data. Accordingly, the data warehouse (DW) for Google is able to analyze the extensive data with high efficiency and accuracy. Google has inbuilt architecture characterized by clusters amounting to 15,000 PCs with software that is fault-tolerant. When cluster computation has been developed to produce high efficiency, the central repositories generate data with more speeds even in instances where the sources are disparate. MapReduce algorithm allows for speedy processing of computations, particularly when the amounts of data are massive and the execution anticipated is on a large scale generated from different clusters. MapReduce as a programming model enables the data warehouse to process
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