Music streaming Analytics
During the course 183.243 Advanced Software Engineering at Vienna University of technology an analytics project for data generated by music streaming royalties was conducted.
The intention was to use anomaly detection to flag unprecedented events, i.e. either platforms having a billing problem or artists gaining too much traction which might indicate fraud.
The project was built using Apache Spark and Spark Jobserver connected via a middleware powered by Apache Camel and a frontend written in Angular JS with Typescript.