Effective and Efficient Way of Reduce Dependency on Dataset with the Help of Mapreduce on Big Data

Satish Londhe, Smita Mahajan


With the fast development of networks these days organizations has overflowing with the collection of millions of data with big number of combination. This big data challenges over trade troubles. It requires more analysis for the high-performance procedure. The new method of hadoop and MapReduce methods are discussed starting the data mining standpoint. In the proposed research work we have to progress performance through parallelization of different operations such as loading the information, index building and evaluating the queries. Thus the performance analysis is completed with the minimum of three nodes with in the Amazon cloud environment. Hbase is a open source, non-relational and distributed database model. It executes on the pinnacle of Hadoop. It consists of a single key with multiple values. Looping is avoid in retrieving a meticulous data from huge datasets and it consume less amount of time for execute the data. HDFS file system is used to store the data after performing arts the map reduces operations and the execution time is decreased when the amount of nodes gets increased. The performance analysis is tuned with the parameters such as the carrying out complexity.


DOI: http://dx.doi.org/10.11591/telkomnika.v15i1.8080

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