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.
Mondal et al. (Thu,) studied this question.