Abstract

Big Data Analytics is a way of extracting value from these huge volumes of information, and it drives new market opportunities and maximizes customer retention. The rapid rise of the Internet and the digital economy has fuelled an exponential growth in demand for data storage and analytics, and IT department are facing tremendous challenge in protecting and analyzing these increased volumes of information. The reason organizations are collecting and storing more data than ever before is because their business depends on it. The type of information being created is no more traditional database-driven data referred to as structured data rather it is data that include documents, images, audio, video, and social media contents known as unstructured data or Big Data. This paper primarily focuses on discussing the various technologies that work together as a Big Data Analytics system that can help predict future volumes, gain insights, take proactive actions, and give way to better strategic decisionmaking.

Highlights

  • Big Data Analytics reflect the challenges of data that are too vast, too unstructured, and too fast moving to be managed by traditional methods

  • Analytics has become inextricably vital to realize the full value of Big Data to improve their business performance and increase their market share

  • In designing “Big Data” analytics systems, we summarize seven necessary principles to guide the development of this kind of burning issues [3]

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Summary

A REVIEW STUDY ON BIG DATA ANALYSIS USING R STUDIO

Savita 1, Neeraj Verma 2 1 MTech Scholar, Dept of Computer Science & Engineering PPIMT, Hisar (Haryana), India 2 Assistant Professor, Dept of Computer Science & Engineering PPIMT, Hisar (Haryana), India Abstract: Big Data Analytics is a way of extracting value from these huge volumes of information, and it drives new market opportunities and maximizes customer retention. This paper primarily focuses on discussing the various technologies that work together as a Big Data Analytics system that can help predict future volumes, gain insights, take proactive actions, and give way to better strategic decisionmaking. Cite This Article: Savita, and Neeraj Verma.

Introduction
Big Data Concept
Big Data Problem and Challenges
Principles for designing Big Data System
Big Data Opportunities
Big Data Analysis
Literature Review
Volume
Velocity
Variety
Big Data Tools
R Programming
Comparisons of Classification for Big Data Science
The importance of Big Data
Findings
Conclusions
Full Text
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