Abstract

As the volume, velocity, and variety of data continue to grow exponentially, optimizing big data analytics workflows on cloud platforms has become paramount for organizations seeking efficient data processing and insightful analytics. This paper investigates various strategies and techniques for enhancing the performance, scalability, and cost-effectiveness of big data analytics workflows in cloud environments. Through a comprehensive review of literature and empirical analysis, we explore key optimization approaches including resource provisioning, data partitioning, parallel processing, and workload scheduling. We also examine emerging technologies such as serverless computing, containerization, and auto-scaling, and their impact on optimizing big data analytics workflows. By synthesizing existing research and practical insights, this study aims to provide guidance and best practices for practitioners and researchers in effectively harnessing the power of cloud platforms for big data analytics.

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