This analysis compares microservices and monoliths in high-performance data analysis tools, suggesting key architectural selections.
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.
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N. Shrivastava (2025) studied this question.