This project was part of my final thesis for my Professional Bachelor’s degree in Software Development in January 2020. The focus of the project was to explore how data can be collected from vehicles and how such data can be used to improve safety and optimize the transportation industry.
The project included research and analysis, where I investigated how data can be collected from vehicles and worked with the 5Vs of big data analysis, big data lifecycle analysis, scalability considerations using the AKF Scale Cube, cost–benefit analysis, cross-functional team analysis, risk analysis, and the definition of a software testing strategy.
As part of the project, I developed a prototype system for a predictive maintenance use case focused on anomaly detection in vehicle sensor data to identify faulty vehicle components. The prototype was developed using the SCRUM agile development methodology and consisted of three sprints, each lasting 14 days.
The final prototype system consisted of a platform built on top of Apache Kafka and Confluent Platform to provide a highly scalable data streaming infrastructure. I used the Java Spring Framework to develop application functionality, combined with Kafka Streams and ksqlDB to process streaming workloads. An opt-out mechanism for connected vehicles was also implemented to address data protection concerns and ensure GDPR compliance.