Leveraging Data Analytics by Transforming Relational Database Schema in to Big Data

The growth of data and its efficient handling is becoming more popular trend in recent years bringing new challenges to explore new avenues. Data analytics can be done more efficiently with the availability of distributed architecture of “Not Only SQL” NoSQL databases. Technological advancements around us are changing very rapidly and major shift is being carried out, a shift from relational to non-relational world. More precisely we are talking about the shift from traditional relational database models to non-relational database models. When moving from relational to non-relational models, database administrators face common issues due to the fact that NoSQL is a No-Schema database. Logical mapping of the schema from relational to non-relational models is complex and it is not a standard process. The purpose of conducting this research is to propose a mechanism by which the schema of a relational database management system and its data can be transformed into big data by following a set of standardize rules. This model can be very useful for relational database administrators by enabling them to focus on logical modeling instead of procedural writing for every SQL to NoSQL transition. In this paper, we studied both models and focus our research to present a set of rules and framework that can be used to apply transformation operation in a seamlessly manageable way.