A Cloud based Remote Diagnostics service for Industrial Paper Mill

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master_Khan_Muhammad_2020.pdf (4.97 MB)

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Perustieteiden korkeakoulu | Master's thesis
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Authors

Khan, Muhammad

Date

2020-12-14

Department

Major/Subject

Computer Science

Mcode

SCI3042

Degree programme

Master’s Programme in Computer, Communication and Information Sciences

Language

en

Pages

46

Series

Abstract

This is an era where data is being generated by several devices around the clock which accumulates to petabytes. Industrial equipment are also generating telemetry data which is used to gain insights of industrial processes. Telemetry data also help perform remote diagnostics, early failure detection and incident cause discovery. This leads to development of big data processing systems which help answer how the data should be stored and processed. There are numerous existing architectures for big data processing systems, one of which is Lambda Architecture which provides a way to implement a distributed data processing system which can be customized according to the business needs. Lambda Architecture can also be complex to implement and maintain due to the combination of two processing systems in a single architecture. In this thesis, we propose and implement a robust and simplified approach for developing the data processing system using Lambda Architecture. The proposed approach strives to use minimum number of services limiting to Azure CosmosDb, Apache Spark, Azure EventHubs and Azure Functions to implement this complex architecture. We show that our approach makes the data processing system maintainable and reduces infrastructure management.

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Supervisor

Främling, Kary

Thesis advisor

Mazhar, Fawad

Keywords

cloud architecture, data pipeline, remote diagnostics, lambda architecture, Microsoft Azure

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https://urn.fi/URN:NBN:fi:aalto-2020122056388

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[dipl] Perustieteiden korkeakoulu / SCI