Linking Conceptual and Logical Data Models in Metadata

How to maintain traceability and automation between business concepts and implementation models using YAML

🧩 Metadata-Driven Modeling Series

A series exploring how modern data platforms connect business meaning and technical implementation using metadata-as-code.
1️⃣ From Business Concepts to Database Blueprint — bridging conceptual and logical models inspired by Alec Sharp.
2️⃣ Linking Conceptual and Logical Data Models in Metadata (this article)— maintaining traceability and automation between business concepts and implementation models using YAML.
3️⃣ Generating Logical Models Automatically from Conceptual Metadata — implementing Alec Sharp’s normalization patterns (1NF–2NF–3NF) in Python.

Summary

In the previous article, we explored how conceptual and logical data models evolve — from business understanding to technical precision — drawing on Alec Sharp’s Business-Oriented Data Modelling principles.

In this follow-up, we’ll translate that philosophy into metadata-as-code: how to represent both conceptual and logical data models in YAML, and how to link them to preserve business meaning all the way down to the database layer.

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