Research Article

A Comparative Analysis of Outline of Tools for Data Mining and Big Data Mining

Authors

  • Rabi Sankar Mondal Master of Science in Business Analytics, (University of New Haven, CT, USA), Master of Pharmacy (Jamia Hamdard, New Delhi, India), Bachelor of Pharmacy (Jamia Hamdard, New Delhi, India)
  • Md. Nazmul Alam Bhuiyan MBA in Data Analytics, (University of New Haven, CT, USA), Bachelor of Business Administration (East West University, Bangladesh)
  • Md. Kamruzzaman MBA in Data Analytics, (University of New Haven, CT, USA), Master of Business Administration, Accounting & Information Systems (University of Dhaka, Bangladesh), Master of Social Science, Political Science (National University, Bangladesh), Bachelor of Social Science, Political Science (National University, Bangladesh)
  • Sujoy Saha Master of Science in Business Analytics, (University of New Haven, CT, USA), Master of Science in Statistics, (National University, Bangladesh), Bachelor of Science in Statistics, (National University, Bangladesh)
  • Md. Shoeb Siddiki MBA in Data Analytics, (University of New Haven, CT, USA), Master of Business Administration (Dhaka International University, Bangladesh), Bachelor of Business Administration (Dhaka International University, Bangladesh)

Abstract

This paper presents a comprehensive comparative analysis of tools used in big data, data mining, and data analytics domains. As data volumes continue to grow exponentially, organizations face increasing challenges in effectively storing, processing, and extracting valuable insights from diverse datasets. Through a systematic literature review and empirical evaluation, we examined 87 distinct tools across multiple dimensions, including technical architecture, processing paradigm, scalability characteristics, deployment models, and cost-benefit considerations. Our findings reveal a trend toward specialization rather than consolidation, with significant performance tradeoffs across different architectural approaches. In-memory processing frameworks demonstrated substantial advantages over disk-based alternatives, while hybrid processing paradigms attempted to bridge the gap between batch and stream processing with varying degrees of success. Notably, all tool categories exhibited diminishing returns in scaling efficiency beyond certain cluster sizes, with machine learning platforms showing particular limitations due to model synchronization bottlenecks. Cloud-based deployments offered superior agility and reduced setup time but at the cost of decreased cost predictability and data sovereignty. Our analysis further indicates that open-source solutions provide better performance per dollar for technically sophisticated organizations, while commercial platforms accelerate time to value for those with limited internal expertise. This research contributes to both practitioner and academic communities by providing evidence-based guidance for tool selection aligned with specific organizational requirements and identifying critical areas for future research and development in big data technologies.

Article information

Journal

Journal of Business and Management Studies

Volume (Issue)

7 (4)

Pages

232-242

Published

2025-07-31

How to Cite

Rabi Sankar Mondal, Md. Nazmul Alam Bhuiyan, Md. Kamruzzaman, Sujoy Saha, & Md. Shoeb Siddiki. (2025). A Comparative Analysis of Outline of Tools for Data Mining and Big Data Mining. Journal of Business and Management Studies, 7(4), 232-242. https://doi.org/10.32996/jbms.2025.7.4.14

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Keywords:

Big Data, Data Mining, Data Analytics, Comparative Analysis, Performance Evaluation, Scalability, Cloud Computing