Optimization and standardization of data transformations of the credit risk management model

The ETL process is a key element in modern data handling, allowing data to be extracted, transformed and uploaded into final systems. This model is often part of a robust enterprise solution for data extraction. Although many teams and departments would like to see a more flexible implementation, its adoption based on an agile citizenship development approach carries certain risks. The structure is vulnerable to problems associated with its complexity and difficulty to administer. This thesis deals with the data transformations optimization written in bank PL/SQL code based on the citizenship development approach. The aim of the thesis is to optimize the existing ETL structure in order to make it more organized and easier to understand. The optimization of the model focuses on restructuring the ETL hierarchy, the efficiency of the written SQL code, and data cleaning and organization according to best practices. The work also focuses on the efficiency of data processing and value handling. Data quality is then evaluated based on these principles. The contribution of this thesis is also the definition of a set of best practices built on a modular solution for newly emerging datasets.