Software developers have started to take NoSQL data storage solutions into consideration in context of the big data’s demanding requirements. The performance of a NoSQL database in terms of speed of data access and processing, particularly response times to the most crucial CRUD activities, is one of the key factors to consider when choosing a NoSQL database for an application (CREATE, READ, UPDATE, DELETE). In this study, the behavior of two important databases-Oracle, a well-known SQL database, and MongoDB, a document-based NoSQL database-will be examined in terms of the complexity and effectiveness of CRUD operations, particularly in query operations. The primary goal of the study is to conduct a comparative examination of the effects that each unique database has on the efficiency of the application when processing CRUD queries. A case-study application for both of the databases that aims to model and simplify the operations of organizations that use massive data, this application is designed using Python. The findings demonstrate how both the databases perform for various data volumes. Based on these, a thorough analysis and a number of conclusions are offered to aid in the decision-making process for selecting an acceptable solution for use in big data applications.

MongoDB, Oracle, SQL, NoSQL database, Performance, CRUD operation