Understanding Physical, Logical, And Conceptual Data Models (medallion Architecture Perspective)

2026-04-23 · Source: Data Engineering on Medium · Field: Technology & Digital — Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, short

Summary

This article clarifies the distinctions between conceptual, logical, and physical data models, emphasizing their importance in modern data architectures like the Medallion Architecture (Bronze, Silver, Gold). The conceptual model defines what the business cares about, such as "Customer" or "Order," without technical details. The logical model translates these business ideas into structured components like tables, columns, data types, and relationships, focusing on data quality and consistency. The physical model dictates how data is actually stored, including file formats (e.g., Parquet), storage locations (e.g., S3), partitioning, and compression, directly impacting performance and cost. The Medallion Architecture maps these models: Bronze is physical-heavy, Silver focuses on logical structuring, and Gold exposes conceptual, business-ready data. Data issues often arise from disconnects between these layers.

Key takeaway

For Data Engineers building or maintaining data pipelines, understanding the interplay between conceptual, logical, and physical data models is crucial. Your ability to connect business meaning to structured data and optimized storage directly impacts data quality and stakeholder trust. Ensure your Medallion Architecture layers clearly map to these models to prevent disconnects that lead to inconsistent metrics or performance bottlenecks.

Key insights

Effective data systems align conceptual, logical, and physical data models for seamless data flow.

Principles

• Conceptual model defines business meaning.
• Logical model structures data for usability.
• Physical model optimizes storage and performance.

Method

Data flows from raw files (physical/Bronze) to structured tables (logical/Silver), then to aggregated, business-ready insights (conceptual/Gold) for dashboards.

In practice

• Trace data issues across all three layers.
• Align Medallion layers with model types.
• Optimize storage for cost and performance.

Topics

• Conceptual Data Model
• Logical Data Model
• Physical Data Model
• Medallion Architecture
• Data Engineering

Best for: Data Engineer, Analytics Engineer, MLOps Engineer

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• Logical First, Physical Second: A Pragmatic Path to Trusted Data — Effective data architecture prioritizes business meaning and shared semantic models over immediate physical schema design.
• What Really Happens Between Bronze, Silver, and Gold Layers? — Medallion Architecture defines clear responsibilities for raw, trusted, and business-ready data layers, simplifying data pipeline management.
• DBMS Data Models Explained: Types, Abstraction Levels, and SQL Examples — Effective data models are crucial for scalable, reliable, and consistent structured storage systems.
• What Is Medallion Architecture? Bronze, Silver & Gold Explained — Medallion Architecture structures data into Bronze, Silver, and Gold layers for quality, governance, and accessibility.
• Main Data Warehouse Components Explained (2025) — Data warehouses organize vast datasets into actionable insights using a layered architecture and dedicated processing.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.