Event-Driven Architectures for Real-Time Analytics in Big Data Systems

The buzzword in big data processing is Event-Driven Architecture (EDA), which is proving to be an effective alternative to processing data in real-time. As we know, the traditional batch processing methods are not enough to meet the demand for real-time analytics owing to the exponential growth of Big Data. Event-driven architecture provides a loosely coupled and scalable implementation, as it decouples the components of a system, where data flows between them asynchronously in response to events. We have built upon the principles of EDA and its applications in the domain of real-time analytics for Big Data. There are three moving parts to an EDA: an event producer, an event broker, and an event consumer. On the event producer, you will create events according to when the data changes in data or drive an event based on the user acting; it gets saved into the event broker. The consumer then consumes this data, processes events and produces insights in real-time. EDA has several benefits for big data systems. It is able to process large amounts of data simultaneously in real-time to facilitate timely decision-making for organizations. It has fault tolerance and horizontal scalability so that it can handle spikes well. It also offers greater ease of processing events and improved integration with diverse data sources. Pervasive phenomenon of Big Data: with, EDA is emerging as a viable contender in real-time analytics processing.