April 30, 2025

Understanding Data Models: Conceptual, Logical, and Physical Design Explained

When developing real-world applications, one of the first questions we need to ask is:
“How will my users interact with the data in this application?”

Will users input data that needs to be saved? Will they need to query or retrieve that data later?
In most applications, the answer to all these questions is a resounding yes—which is why database design and development are foundational to building reliable, scalable software.

A well-designed database begins with a clear understanding of data modeling, the process of defining how data is stored, organized, and accessed. There are three core data models involved in this process: conceptual, logical, and physical. Each plays a distinct role in turning business requirements into functioning database systems.

1. Conceptual Data Model: The “Whiteboard” Phase

The conceptual model is the high-level, abstract representation of your data. Think of this as the “whiteboard” stage—it’s often sketched out during brainstorming sessions, and it doesn’t concern itself with technical details or implementation specifics.

At this stage, we define:

  • Entities (e.g., users, products, transactions)
  • Attributes of those entities (e.g., name, email, price)
  • Relationships between entities (e.g., one user can place many orders)

A common tool used in this phase is the Entity-Relationship Diagram (ERD). The goal is to map out the key components of the data system in a way that is understandable to both technical and non-technical stakeholders.

As Groves (2022) notes, the conceptual model is not about the “how”—it’s about the what.

2. Logical Data Model: Defining the Structure

Once the conceptual model is solid, we move to the logical data model. This is where we start getting into the structure and rules of the database, without worrying yet about the specifics of the database system (e.g., MySQL vs. PostgreSQL).

In the logical model, we specify:

  • Data types for each attribute (e.g., VARCHAR, INTEGER, DECIMAL)
  • Field sizes and constraints (e.g., max length of a string)
  • Cardinality and relationships (e.g., one-to-many, many-to-many)

For example, if we have an employees table, we might define a name column as VARCHAR(25) to support names of up to 25 characters. We also define primary and foreign keys that structure relationships between tables.

This phase ensures that your data is consistent, normalized, and ready for implementation.

3. Physical Data Model: Implementation in the Real World

The physical model is where your database becomes a reality. This is the stage where you take all the decisions made in the logical model and translate them into SQL code that creates the actual schema in your chosen database system.

At this point, you're deciding:

  • Which database platform to use (e.g., PostgreSQL, MySQL, MongoDB)
  • Indexes, partitioning, and storage considerations
  • The exact SQL DDL (Data Definition Language) needed to build tables, define relationships, and apply constraints

This is where developers begin running SQL scripts to spin up the production-ready database.

A Real-World Example: Stock Market Application

Let’s walk through a practical example to see these models in action.

Scenario: You're building a stock trading application that helps users decide when to buy, sell, or hold various assets.

Conceptual Model

You might start by sketching:

  • An assets table with stock tickers and IDs
  • A historical_prices table to track daily prices for each stock

Logical Model

Next, you define:

  • assets.id as an INTEGER
  • assets.ticker as a VARCHAR(12)
  • historical_prices.price as a DECIMAL(6,2) to support values like 120.22
  • Relationships: historical_prices.asset_id is a foreign key referencing assets.id

Physical Model

Finally, you implement this in your database:

  • Choose PostgreSQL for its strong relational capabilities
  • Write SQL scripts to create both tables and establish the necessary keys and constraints
  • Deploy the schema and begin inserting or querying data

Final Thoughts

Understanding the differences between conceptual, logical, and physical data models is essential for designing effective and scalable databases. Each layer builds on the previous one, turning abstract ideas into concrete systems.

Whether you're developing a fintech app, an e-commerce platform, or a machine learning pipeline, mastering data modeling ensures your application can handle data efficiently, accurately, and reliably.

References

Groves, M. (2022, October 7). Data modeling explained: Conceptual, physical, logical. Couchbase.
https://www.couchbase.com/blog/conceptual-physical-logical-data-models/