The rapidly increasing amount of digitalization and data collection in the industrial sector increased the need for data processing. Processing the collected information and measurements can yield vast amounts of useful information, which in turn can improve product quality, can filter out hard to detect errors, and can also reduce the time required to fix errors turning up during manufacturing.
The task of this thesis is to design a system capable of processing large amounts of data, creating understandable insights, notifying the designated persons in the event of malfunctions.
As processing large amounts of data requires large amounts of computing- and storage capacity, cloud services are used to realize the system, as such it can be easily scaled to real-life applications, due to quick and simple scalability.
The system can transport data messages to a cloud service or to another processing system from multiple devices, where it gets stored and processed. After processing the system is able to create visualizations using the most recent data, or previously processed and stored data. All these steps are implemented with the appropriate Microsoft Azure cloud services.