Cloud-based big data platforms are essential for managing the growing volumes and complexities of moderndata.Despite their scalability and flexibility, optimizing processing speed within these platforms remains a significant challengeduetoissues such as network latency, resource provisioning, and configuration complexity. This paper explores the methods employedtoenhance processing speed in cloud-based big data systems, identifies the primary challenges faced, and proposes arangeofpractical solutions. We examine techniques such as data locality optimization, in-memory processing, resource auto-scaling, andparallelism, and discuss the trade-of s and challenges associated with each approach. The findings suggest that whileoptimizingspeed requires careful consideration of system architecture, resource management, and fault tolerance, there are several effectivestrategies that can lead to significant performance improvements without incurring prohibitive costs. Keywords: Cloud computing, big data, parallelism, optimization, task scheduling