Author(s): Michael A Turner

Abstract: Small-scale application development has evolved rapidly due to increasing demands for flexibility, scalability, and faster deployment cycles. Data management plays a central role in these applications, influencing performance, maintainability, and long-term adaptability. Traditionally, relational database management systems have dominated application development because of their structured schemas, strong consistency guarantees, and mature query capabilities. However, the emergence of NoSQL data models has introduced alternative approaches designed to handle unstructured data, horizontal scalability, and agile development requirements. This review examines the comparative strengths and limitations of relational and NoSQL data models in the context of small application development. Key aspects such as data modeling flexibility, query expressiveness, scalability mechanisms, consistency models, development complexity, and operational overhead are critically analyzed. The study highlights how relational models remain effective for applications requiring complex relationships, transactional integrity, and standardized query support, while NoSQL models provide advantages in schema flexibility, rapid iteration, and scalability for data-intensive or evolving workloads. Particular emphasis is placed on decision-making factors relevant to small development teams, including resource constraints, ease of maintenance, and future growth considerations. By synthesizing findings from prior empirical and conceptual studies, this review aims to clarify common misconceptions surrounding database selection and to provide a balanced perspective on model suitability. The analysis suggests that neither approach is universally superior; instead, the choice depends on application requirements, data characteristics, and expected evolution. The review concludes that informed database selection, aligned with project scale and objectives, can significantly enhance development efficiency and application reliability in small-scale software projects while reducing technical debt and long-term operational risks.

DOI: 10.33545/27076636.2026.v7.i1a.149

Pages: 21-25 | Views: 109 | Downloads: 64

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