Case Study on Evaluating Big-Data Storage Solutions

Abstract

The amount of knowledge hold on is apace increasing because of shopper, business, scientific, and government generated content. Additionally, to keeping pace with storing generated knowledge, there is a requirement to fits laws and repair Level Agreements (SLA) to shield and preserve hold on knowledge. The problems of capability and scale in knowledge storage square measure of constant concern to confirm the power to soak up knowledge growth, to manage existing knowledge and to research the info. Evaluating knowledge storage for IT infrastructure could be a complicated task that has multiple variables. These variables embrace capability, quantifiability, financial, work needs, security, privacy, handiness, responsibility, analytics and operational. Current frameworks that plan to address knowledge storage evaluations focus either on one side of those variables or use generic IT frameworks to judge knowledge storage as a sub-component. The quality information of storage necessities deserves a holistic framework within the data storage domain. The contribution of this paper is an integrated framework to judge and assist in choosing optimum storage answer for multi-variable necessities. This paper examines data dispersion rule storage technology victimization this framework and is that the 1st in an exceedingly series to look at four totally different big-data storage technologies victimization this framework.