SDLC-Independent Python-Based Query Performance Benchmarking Approach and Practical Optimal Database Selection Guidelines

One of the critical decisions of software development teams in application development is choosing the most optimal database.Modern business conditions require software development teams to continually improve application performance, and a common dilemma is whether to transition from a relational database to a non-relational database or vice versa.Changes during the application development phase can slow down and complicate the process, so it is crucial to empirically approach data analysis and decision-making before implementing any changes.This research aims to facilitate the optimal database selection by following established practical guidelines for optimal database selection and implementing an SDLC-independent Python-based query performance benchmarking approach.This benchmarking approach is a crucial part of the optimal database selection process particularly useful in the early stages of development or when considering a migration to an existing project.The research methodology includes qualitative and quantitative methods: analytical-synthetic, experimental, comparative analysis, and hypothetical-deductive methods.The results of this research include the practical optimal database selection guidelines, the process of conducting the benchmark, and the utilization of both the guidelines and benchmark results for optimal database selection of an application where changing the initially selected MySQL database to MongoDB is being considered.