Comparative Analysis of SQL and NoSQL Databases: Data Models, Use Cases, and Performance Insights - Ying, Dionne Teh Wooi; Jie, Choo Yan; Shaoren, Cheah; Eui, Hong Chang; Ken, Chin Wey; Easwaramoorthy, Sathishkumar Veerappampalayam; Moorthy, Usha (Information Research Communications, 2025)
Aim/Background The primary aim of this study is to conduct a comparative analysis of SQL and NoSQL databases based on their data models, performance characteristics, and suitability for various application scenarios. It specifically investigates relational, key-value, graph, document, and wide-column models, focusing on their operational implications, such as data integrity, query performance, scalability, and security. Methodology This research adopted a mixed-methods approach, combining qualitative and quantitative evaluations. It involved literature review, official DBMS documentation, and performance benchmarking. The study utilized five DBMSs-Oracle (SQL), Neo4j (graph), Cassandra (wide-column), Redis (key-value), and MongoDB (document). Performance metrics like data creation, manipulation, retrieval, access control, and data integrity were analyzed. Scenario-based analyses (e-commerce and social media analytics platforms) were used to examine database suitability under different real-world conditions. Results The results indicated that NoSQL databases generally outperformed SQL databases in terms of scalability, data flexibility, and runtime performance, especially under large-scale data operations. SQL databases like Oracle demonstrated strong data integrity and complex querying capabilities but lagged in scalability and schema flexibility. NoSQL databases like MongoDB and Neo4j provided ACID compliance with dynamic schemas, while Redis and Cassandra excelled in high-speed data operations with eventual consistency. Scenario analysis confirmed the contextual suitability of each model. Discussion While NoSQL databases offer superior performance for unstructured data and scalable applications, SQL databases remain indispensable for structured, transaction-heavy systems due to their robust consistency and integrity mechanisms. The preference for a database model depends heavily on application context, data structure, and performance requirements. The findings highlight that no single model is universally superior; rather, optimal selection depends on the specific use case. Conclusion The study concludes that both SQL and NoSQL databases have distinct strengths and weaknesses. SQL databases are best suited for structured, transactional systems, whereas NoSQL models are ideal for modern, data-intensive, and scalable applications. Organizations should evaluate their specific data requirements and system demands when selecting an appropriate database solution.
Keywords: SQL; NoSQL; Oracle; Cassandra; Redis; Neo4j; MongoDB; Relational model; Key-value model; Graph model; Wide-column model; Document model.