Event-Driven Data Processing for Business Intelligence Reporting
Abstract: When comparing event-driven data processing versus batch processing, the former adds important insights. Routers, consumers, and producers are three crucial elements of event-driven architecture to collaborate to guarantee precise and prompt data analysis and transmission. Businesses may use real-time data analytics to find new patterns and trends. Moreover, the difficulties with complexity and inconsistent information. Businesses may handle efficient BI reporting and event-driven data processing by implementing strategies like separate data volumes and the auto-scaling mechanism.
Keywords: Auto-scaling, batch processing, real-time data analytics, microservice architecture, functional hierarchy
How to Cite?: Girish Ganachari, "Event-Driven Data Processing for Business Intelligence Reporting", Volume 8 Issue 12, December 2019, International Journal of Science and Research (IJSR), Pages: 2077-2080, https://www.ijsr.net/getabstract.php?paperid=SR24801085403, DOI: https://dx.doi.org/10.21275/SR24801085403