µBIGMSA-Microservice-Based Model for Big Data Knowledge Discovery: Thinking Beyond the Monoliths

Enterprise thrives on software applications that are built to fulfil the core business requirements. A single business application can offer a cluster of capabilities to generate value from processing huge amount of data often termed as Big Data. The time-based requirements of these applications are satisfied frequently by applying monolithic approaches with increased complexity and less scalability. Traditional approaches for Big Data Analytics suffer from overpriced, excessive and irrelevant data transfer owing to the constricted coupling amongst computing resources and data processing logic. Service-oriented approach came into existence as a new paradigm to enable applications to be rendered as service for better flexibility and scalability. Service orientation architecture avoids monolithic style but web services, one of its major implementation encourages monolith development of software application. Thus building a scalable, robust, resilient, cost-effective and optimum solution is one of the major requirements for outsized data. New software development style Microservices offer low degree of coupling and smaller size. This work reviews the existing and prevalent approaches like monolithic architecture in this area along with their drawbacks. This work also proposes a generic microservice model µBIGMSA for handling Knowledge Discovery in Big Data. Reference applications are implemented using proposed model. The effectiveness of the proposed model is evaluated by comparing the reference application with the monolithic application using various software metrics.