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Abstract The crucial role of competent software architecture is essential in managing the challenging big data processing for both relational and nonrelational databases. Relational databases are designed to structure data and facilitate vertical scalability, while non-relational databases excel in handling vast volumes of unstructured data and are geared towards horizontal scalability. Choosing the right database paradigm is determined by the needs of the organization, yet selecting the best option can often be challenging. 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 data is the most important element in maintaining organizational growth, selecting the right nonrelational database 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.
Fahd et al. (Thu,) studied this question.