Data Engineering In Azure For Efficiently Connecting Data In Credit Risk Domain

This thesis proposes a dynamic architecture for data engineering in the credit risk domain, utilizing Microsoft Azure to provide a one-stop solution for multiple data engineering needs. The proposed architecture is restricted to the financial/credit risk domain and can be further improved with the latest tech tools. The practical use of the proposed architecture is limited to Atradius but can be extended to multiple domains trying to achieve data engineering. The proposed architecture aims to achieve complete data ingestion with automation, providing a working solution for Atradius to dynamically and smoothly achieve data engineering in everyday tasks. The limitations of the current architecture and suggestions for future improvements are highlighted. The thesis supervisors, family, and friends are acknowledged for their valuable guidance and support.