Comparative Performance Analysis of SQL and NoSQL Databases for Optimizing Retail and E-Commerce Operations

Accelerated by the COVID-19 pandemic, the rapid growth of online retail, or electronic commerce, has transformed the way consumers fulfil their needs and desires. This expansion introduced new business opportunities that expedites international market growth. The digitalization of economy and consumer behaviour necessitates advancements in technologies and the selection of a database to process incoming customer data. The exponential increase in data has also increased the demand for robust and scalable database management systems. This paper aims to select the most suitable data model for online retail industries by conducting comparative analysis between relational, graph, wide-column, key-value, and document database models. The study explores the performance, strengths, and weaknesses of each model in handling various aspects of retail and e-commerce functionalities, including order processing and customer relationship management (CRM). Each database model is rigorously tested against thousands of records to accurately assess their effectiveness and performance within the retail industry. After deliberate testing, it is discovered that relational databases outshine the other NoSQL databases in terms of client performance - CPU speed in GHz, the CPU utilization and memory usage in percentage, and its capacity in handling large datasets in the e-commerce industry.