The architecture of modern data analysis tools must meet the demands of high performance, scalability, modularity, and maintainability. Traditionally, monolithic architectures dominated the software landscape. However, microservices have emerged as a compelling alternative, especially for systems handling large-scale simulation, analytics, and visualization. This paper compares the two architectural styles—monoliths and microservices—in the context of high-performance data analysis tools. We examine design principles, performance trade-offs, fault tolerance, and scalability, and present guidelines for selecting the appropriate architecture based on system requirements.
N. Shrivastava (Wed,) studied this question.