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Abstract: This article presents a comprehensive case study on optimizing big data pipelines within the Amazon Web Services (AWS) ecosystem to achieve cost efficiency. We examine the implementation of various cost-saving strategies at Amazon, including right-sizing EC2 instances, leveraging spot instances, intelligent data lifecycle management, and strategic reserved instance purchasing. Through quantitative analysis of real-world scenarios, we demonstrate significant reductions in AWS compute costs while maintaining performance and scalability. The article reveals that a combination of these approaches led to a 37% decrease in overall operational expenses for Amazon's big data processing infrastructure. Furthermore, we discuss the challenges encountered during optimization, the trade-offs between cost and performance, and provide actionable insights for organizations seeking to maximize the value of their AWS investments. Our findings contribute to the growing body of knowledge on cloud resource optimization and offer practical guidelines for enterprises managing large-scale data processing workloads in cloud environments.
Chilukoori et al. (Fri,) studied this question.