Speedy Management of Data Using MapReduce Approach

Abstract

The previous contribution utilizes two assignments in practical programming: Map and Reduce. MapReduce is another preparing structure. Hadoop is its open-source execution on a solitary processing hub or groups. Contrasted and existing preparing ideal models, MapReduce and Hadoop enjoy two benefits. The deficiency lenient capacity brings about dependable information handling by reproducing the registering errands and cloning the information lumps on various figuring hubs across the processing bunch. The high-throughput knowledge preparation employs a cluster handling structure and the Hadoop disseminated document framework. Information is put away in the HDFS and made accessible to the slave hubs for calculation. The suggested work uses hashing methodology to increase speed in searching the required data. The suggestion reduces the mapping using hashing method based on attributes by 29.6% compared to previous work.

Key Terms in this Chapter

MapReduce: It is a programming approach used to manage large set of data.

Hashing: This is a methodology where the input string is converted to another form.

Attribute: The property of information/input.