Big Data computing has become a cornerstone technology driving digital transformation across industries. This paperprovides a comprehensive exploration of Big Data computing paradigms, architectural frameworks, processing technologies,and contemporary challenges. We examine the evolution from traditional data warehousing to modern cloud-nativearchitectures, analyze key processing frameworks including Apache Spark, Hadoop, Flink, and Kafka, and discuss real-timeanalytics capabilities. Furthermore, this paper addresses critical challenges including data privacy, security, scalability, andregulatory compliance, while highlighting emerging trends such as AI-ML integration, federated learning, and edge computing.Our findings demonstrate that hybrid approaches combining on-premise and cloud solutions are becoming mainstream, withapproximately 65% of enterprises adopting Hadoop and Spark in tandem. This research concludes by identifying future researchdirections necessary to address emerging complexities in distributed data systems and regulatory landscapes.
Gomathy et al. (Sat,) studied this question.
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