Feedback analysis using big data tools

With the ever increasing man-machine interaction, automation of process and decline in hardware and software cost, the amount of digital data generated and used is increasing day by day. The big data referred here is the massive amount of digital data generated in each and every second in structured, semi-structured and unstructured format throughout the world. This emerging field of big data analytic has driven the researcher worldwide toward design, development and implementation of various tools, technologies, architecture and platforms for analyzing the huge volume of data generated day to day. Big data consist of data sets which is difficult for legacy database management system to analysis. This paper details some analysis like feedback analysis, sentiment analysis and word-count. Feedback are important for the system enhancement, finding loop holes and as well as for proper work distribution. Feedback is valuable information that will be used to make good decision. Feedback is important not only when it highlights weaknesses but also for strengths. If analysis of feedback is done in wrong way then the result of analysis will also be wrong. As a result, the pattern identified will also be incorrect thus making the whole system incorrect as a whole. We will be implementing this proposed system for feedback analysis using Map-Reduce framework for processing large data set and for storage we will use Hadoop.