An Approach in Big Data Analytics to Improve the Velocity of Unstructured Data Using MapReduce

Big data analytics is an innovative approach to extract the data from a huge volume of data warehouse systems. Hadoop is a framework, which is used to perform high speed data retrieval from various clusters by MapReduce and HDFS methods. The huge volumes of files are accessed using data mining, machine learning, and deep learning algorithms. However, these techniques take more time to retrieve the data among the clusters. To overcome the latency issue, the proposed work applies the hybrid algorithm, namely compressed elastic search index (CESI) and MapReduce-based next generation sequencing approach (MRBNGSA), in scheduling and shuffling phase. This proposed approach provides the tangible changes over the MapReduce phases. The performance of the proposed CESI-MRBNGSA algorithm provides significant performance than Hadoop BAM and GATK.