EFFICIENT PARALLEL DATA ANALYSIS: INTEGRATING MAPREDUCE WITH HADOOP DISTRIBUTED FILE SYSTEM

The necessity for effective algorithms for data processing in parallel databases has grown critical in the current era of big data. The purpose of this research is to build an effective algorithm for data analysis in parallel databases. To rapidly analyze massive data sets in parallel, the proposed approach integrates the MapReduce programming model with the Hadoop distributed file system. The algorithm was tested on a real-world dataset, and the findings indicated that it outperformed existing algorithms in terms of execution speed and scalability.

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