Horizontal Scaling Enhancement for Optimized Big Data Processing

Big Data, as we all know, is becoming a new technological trend in the industries, in science and even businesses. Indefinite data scalability allows organizations to process huge amounts of data in parallel, assisting dramatically decrease the amount of time it takes to manage several amount of work, optimize hardware resource usage and permit the extreme quantity of data per node to be handled. Optimization is to done to attain the finest strategy relative to a set of selected constraints which include maximizing factors such as efficiency, productivity, reliability, strength, and utilization. When the current system becomes insufficient, instead of upgrading it by adding more components to the existing structure you just add more computers to a cluster. This research discusses a hierarchical architecture of Hadoop Nodes namely Name nodes and Data nodes and mainly focuses on the optimization of Data Node by distributing some of its work load to Name Node.