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In this case study, we will demonstrate how a large financial institution adopted a data model driven approach to the development its analytical data platform. Starting from a conceptual (fact-based) model, the logical and physical model are generated as well as the DDL code. We will demonstrate the power and merits of each type of model as well as the challenges we faced in putting it to practice. To this end, we created a data modeling pipeline using CaseTalk, ER/Studio, Git and Azure SQL for the end- to-end generation of code. As we will show, apart from the primary goal of model-driven development, following a model- driven approach also facilitated and even initiated fundamental decisions regarding data architecture, model management, handling change and division of responsibilities between data engineers and data modelers. Presented by Wouter van Aerle and Roy Maassen, moderated by Steve Hoberman (Technics Publications)