Music streaming Analytics
Apr 27, 2016
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1 min read
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.
Authors
senior data expert
Georg is a Senior data expert at Magenta and a ML-ops engineer at ASCII.
He is solving challenges with data. His interests include geospatial graphs
and time series. Georg transitions the data platform of Magenta to the cloud
and is handling large scale multi-modal ML-ops challenges at ASCII.