Logical vs Physical Data Model in Modern Data Architecture

In the data projects and their architecture there is essential to understand the distinction and interplay between logical and physical data models is crucial for designing, implementing, and maintaining efficient and scalable databases and data warehouses. These models are foundational elements in structuring data to support various applications, analytics, and business intelligence (BI) projects.

Logical Data Model (LDM)

A logical data model outlines the structure of the data without getting into the technical details of how the model will be implemented. It focuses on the business requirements, different users’ needs and important rules governing the data, presenting entities (tables), and attributes and relationships (as a core element) in a way that’s understandable to business stakeholders. LDMs are used in the first or also called initial phases of project development. Project managers and their stakeholders want to make sure that all business requirements are understood, described and accurately captured and that the data structure is aligned with the organization’s objectives. It serves as a blueprint for the physical model. We are usually aligning to blueprints which are best practices for the core technology when starting our projects. It helps to speed up the processes in the future.

From its characteristics we can find 3 types of Logical Data Models:

· Technology-agnostic