Authors : Olayemi A. Ariyo; Stephen O. Maitanmi

Volume/Issue : Volume 8 - 2023, Issue 7 - July

Google Scholar : https://bit.ly/3TmGbDi

Scribd : https://tinyurl.com/2tthnxt2

DOI : https://doi.org/10.5281/zenodo.8228158

Abstract : This study looked at a variety of Big Data analytical tools, including Tableau, RapidMiner, SAS Enterprise Miner, Apache Mahout, IBM SPSS Modeler, Apache Hadoop, and Apache Spark. Each tool had its own specialties: IBM SPSS Modeler specialized in data visualization and interpretation, SAS Enterprise Miner automated data preparation and model building, Apache Mahout offered scalability for distributed machine learning, Apache Hadoop excelled at handling large datasets, Apache Spark offered high-performance processing and machine learning, and Tableau was a powerful visualization tool as well. It's important to select the appropriate tool based on your unique requirements, level of knowledge, scalability, performance, usability, and practical application. Due to its user-friendly design, huge library of pre-built algorithms, and accessibility for beginners, RapidMiner stood out as the top tool. The selection of a tool, however, is also influenced by unique requirements, technical know-how, and factors like capabilities, scalability, performance, affordability, ease of use, and applicability in the real world. Effective predictive analytics is made possible by the use of the proper tools, enabling priceless insights and well-informed decision-making in the expanding Big Data space. The relevance of predictive analytics in data analysis is highlighted by the ongoing rise of big data, and organizations are urged to use the right tools and methodologies to get insights.

Keywords : Techniques; Analytics; Big Data; Machine Learning; Data Mining.