Paper Title
A Scalable and Reactive Big Data Architecture Design for Predictive Maintenance of Connected Cars

Abstract
Industry 4.0 is transforming cars into computers on wheels; it is of essencefor manufacturers to gain competitive advantage by creating a future-oriented system that can easily integrate with traditional IT systems based on current requirements and unanticipated future growth. This paper provides insights on (i) The architectural aspects of the connected industry, where decisions with respect to maintenance based on massive data are required in fractions of seconds. (ii) Data preprocessing on edge to save on network bandwidth consumption while ensuring minimal information loss. (iii) Application of reactive design principles on big data systems to make them more responsive and maintainable. The results show considerable performance improvement in terms of throughput and scaling compared to conventional big data practices, when implemented on connected car use case. Keywords - Connected Car, Big Data, Scalable Architecture, Reactive Architecture, Predictive Maintenance, Kafka, Spark, Edge Analytics, Cloud Analytics, CQRS