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With the advent of large-scale datasets, visualization has become one of the most useful concepts of data mining, that assists researchers to identify data patterns without manually scanning each of the entry sets. To perform this task, researchers have proposed a wide variety of visualization techniques, that include, Online Analytical Processing (OLAP), different chart types, map types, different graph representation types, etc. But each of these methods vary in terms of their applicability, scalability, and data representation characteristics. Due to which it is difficult for researchers to identify optimum techniques for their application-specific use cases. Moreover, these models showcase variations in terms of their data handling & analysis capabilities, due to which researchers are needed to test & validate different visualization techniques for their deployments. These issues affect selection capabilities of these visualization models, which increases cost & time to market for the underlying product. To overcome these issues, a comparison of these models in terms of their context-specific nuances, application-specific advantages, deployment-specific limitations, and functional future scopes are discussed in this text. Upon referring this discussion, researchers will be able to identify optimum models for their application-specific use cases. This text also compares the models in terms of their performance metrics including delay, complexity, usability, and scalability values, which will further assist readers to identify performance-specific models for different deployments. Thus, this text can be used as a reference model for comparing different visualization tools & techniques for multiple application scenarios. [ABSTRACT FROM AUTHOR]

In recent world data analytics is more popular term in the field of computer science. The paper focuses on the compressive survey of various data analysis tools to connect with the people by just reading their data. Some of the most popular data analysis tools have been chosen, and a comparison has been made based on some key parameters to determine the best tool in the data science industry. Scenes can uses a huge collection of data information to analyze, predict, and deliver information such as the whole determined by various countries, HEIs, government, universities, and schools. In this paper data analytics tools has taken in account with the consideration of different parameter as Cost, data handling capabilities, graphical capabilities, big data, and other considerations. Comparative study has been made using own experiences on various data analysis tools. [ABSTRACT FROM AUTHOR]