Culminating Project Title

Analyzing Big Data Using Hadoop

Date of Award

12-2017

Culminating Project Type

Starred Paper

Degree Name

Information Assurance: M.S.

Department

Information Assurance and Information Systems

College

Herberger School of Business

First Advisor

Changsoo Sohn

Second Advisor

Mark B. Schmidt

Third Advisor

Lynn A. Collen

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Abstract

Due to growing development of advanced technology, data is produced in an increasing rate and dumped without analyzing it. Data sets are coming in large quantities through many mediums like, Networking sites, Stock exchanges, Airplane’s black boxes etc. People who used to have 44 kb small floppy disk in the past are not happy with 1 TB external hard-drives nowadays. Big companies who are forced to add more servers in order to maintain the velocity of the incoming large data sets, are still looking for an easy way to control, handle big data. Traditional methods of handling big data are causing a variety of issues such as slow system performance, and lack of scalability. This research paper explores through the alternative method of handling big data which can address issues of the traditional methods. The goal of this research paper is to highlight an importance of a new method that can replace the traditional method of handling big data. This paper mainly consists of analyzed past work done by several fellow researchers. The outcomes of this paper will be useful for students and researchers alike who would like to work in the field of big data.

Comments/Acknowledgements

I am very much honored to appreciate people who have helped me to complete this research work. I would like to thank Professor Changsoo Sohn, who is also Committee Chair, for his guidance. He was available anytime I needed to work on this research study. I have learned so many things from his class and academic meetings. I would also sincerely like to thank professor Mark B. Schmidt and professor Lynn A. Collen for their guidance to complete this research study. Their time and advise have helped me to accomplish the goal of this research study.

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