Abstract The crucial role of competent software architecture is essential in managing the challenging of big data processing for both relational and nonrelational databases. Relational databases are designed to structure data for facilitating vertical scalability, while non-relational databases excel in handling vast volumes of unstructured data for enhancing horizontal scalability. Choosing the right database paradigm is determined by the needs of the organization, yet selecting the best option may often be a challenging task. Large number of applications still use relational databases due to its benefits of reliability, flexibility, robustness, and scalability. However, with the rapid growth in web and mobile applications as well as huge amounts of complex unstructured data generated via online and offline platforms, nonrelational databases are compensating for the inefficiency of relational databases. Since selecting the right nonrelational database method for high performing applications from a plethora of possibilities is a challenging task, existing studies are still at emergent stage to compare the performance of different popular nonrelational databases. This paper introduces a novel benchmarking approach for tailoring the comparative study of nonrelational databases. To illustrate our approach, we compare two leading non-relational databases, Aerospike and MongoDB, focusing on their average transaction times to evaluate the database performance. Our comprehensive analysis reveals the strengths of each database in read and write operations for single record and bulk record batch transactions.
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Miah et al. (2024) studied this question.
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