Events Sourcing and Command Query Responsibility Segregation Based Fast Data Architecture

Abstract

With the advent of Big Data, the fields of Statistics and Computer Science coexist in current information systems. In addition to this, technological advances in embedded systems, in particular Internet of Things technologies, make it possible to develop real-time applications. These technological developments are disrupting Software Engineering because the use of large amounts of real-time data requires advanced thinking in terms of software architecture. The purpose of this article is to propose an architecture unifying not only Software Engineering and Big Data activities, but also batch and streaming architectures for the exploitation of massive data. This architecture has the advantage of making possible the development of applications and digital services exploiting very large volumes of data in real time; both for management needs and for analytical purposes. This architecture was tested on COVID-19 data as part of the development of an application for real-time monitoring of the evolution of the pandemic in Côte d'Ivoire using Post-greSQL, ELasticsearch, Kafka, Kafka Connect, NiFi, Spark, Node-Red and MoleculerJS to operationalize the architecture.