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Enabling Predictive Maintenance through Efficient Data Warehousing – A Case Study

Jansen, Benjamin (2023) Enabling Predictive Maintenance through Efficient Data Warehousing – A Case Study.

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Abstract:In recent years, Big Data applications have become increasingly prevalent. Advances in technology have led to increasingly sophisticated systems and applications, and the ability of companies to extract meaningful information from data has become a critical factor in a company’s success. Data volumes are growing relentlessly, with estimates that global data volumes are doubling every two years. To minimize unplanned downtime, Malvern Panalytical, a company specializing in analytical instrumentation, wants to adopt a predictive maintenance approach to anticipate maintenance needs. To this end, a large amount of data is collected and stored. Currently, the data is stored unstructured in a raw format. However, to enable predictive maintenance, the data must be stored in a structured and persistent manner, and it must be easily retrievable. This project will develop a data pipeline and data warehouse that will attempt to increase the efficiency and effectiveness of Malvern Panalytical’s data management process. The result of this project is an efficient way to structure and store data and make it available in a way that makes retrieval easy and fast. The ingestion of raw data into the database is fully automated, and data can be retrieved using simple SQL queries. The product is fully compatible with MS Azure. Performance requirements, that mainly focus on ingestion and retrieval speed of data, have been met. The biggest datasets that were available for this project were ingested within less than 4 minutes and retrieved within 11 seconds. Compared to the way the data was previously managed, this project offers significant benefits to Malvern Panalytical, both in terms of performance and cost, and the key findings provide valuable knowledge for other companies in similar situations.
Item Type:Essay (Bachelor)
Malvern Panalytical, Netherlands
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Creative Technology BSc (50447)
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