Dynamic vs. Static visualizations: Understanding their use cases in big data analysis

Dip Bharatbhai Patel *

University of North America, Virginia, United States of America.
 
Research Article
International Journal of Science and Research Archive, 2022, 05(02), 392-395.
Article DOI: 10.30574/ijsra.2022.5.2.0077
Publication history: 
Received on 02 January 2022; revised on 13 April 2022; accepted on 18 April 2022
 
Abstract: 
Big Data has transformed the way organizations derive insights and make decisions. Central to this transformation is the role of visualizations in interpreting complex datasets. Visualizations are broadly categorized into dynamic and static types, each with unique strengths and limitations. Static visualizations offer simplicity and are effective for presenting fixed insights, while dynamic visualizations provide interactivity and adaptability, making them essential for exploratory and real-time data analysis. This paper explores the differences between dynamic and static visualizations, delves into their respective use cases in Big Data analysis, and provides practical guidelines for selecting the appropriate visualization type. The discussion emphasizes the importance of understanding user needs, data complexity, and technological constraints in leveraging these tools effectively. Through real-world examples and case studies, this paper illustrates how dynamic and static visualizations can complement each other to address diverse analytical challenges, enhancing the overall decision-making process in Big Data environments.
 
Keywords: 
Big Data; Visualizations; Dynamic Visualizations; Static Visualizations; Data Analysis; Interactive Data
 
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0