Bernal, Mary CarlotaMolina, Yeimer2022-11-262022-11-262022Bernal, M., & Molina, Y. (2022). Test model for database architectures: an assessment for job search engine systems. Journal of Applied Research and Technology, 20(3), 306-319. https://doi.org/10.22201/icat.24486736e.2022.20.3.116924486736https://hdl.handle.net/20.500.12442/11533Information systems are increasingly complex structures due to the diversity of processes involved and the big data generated, hence data management is essential. NoSQL databases adopt new approaches to data management differing from relational structures. In this study, three databases were designed, a relational database using PostgreSQL and two NoSQL databases made in MongoDB applied to operation of a job offer system, with the aim of comparing its operation and efficiency. A method was proposed for the metric-guided evaluation of database models using functionality and efficiency criteria according to Systems and Software Standard Quality Requirements and Evaluation (SQuaRE). Testing cases were created considering the International Software Testing Qualifications Board (ISTQB) best practices. Relational data model was selected as a pattern, for this reason, to populate NoSQL databases a reference framework was applied for data migration from one environment to another, thus the tests were performed under the same hardware, software and data conditions. This study determined that the SQL schema provides greater functionality, ensuring transaction support and data integrity. On the other hand, the NoSQL schemas are more efficient in response to big data processing, although they have a certain level of data duplication, transaction support fails and some join operations are not support.pdfengAttribution-NonCommercial-NoDerivatives 4.0 InternacionalRelational databaseNoSQLFunctionalityEfficiencyTest modelJob search engine systemsTest model for database architectures: an assessment for job search engine systemsinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://doi.org/10.22201/icat.24486736e.2022.20.3.1169