What is Data Modeling?
Data modeling is the process of creating a visual representation of a system’s data and its relationships. It serves as a blueprint for how data is stored, accessed, and structured in a database or application, ensuring consistency, efficiency, and clarity in data management.
Example of Data Modeling
Consider an e-commerce system:
• Entities: Customers, Orders, Products.
• Attributes:
• Customer: ID, Name, Email.
• Order: Order ID, Order Date, Total Amount.
• Product: Product ID, Name, Price.
• Relationships:
• A Customer places multiple Orders.
• An Order contains multiple Products.
This information would be represented visually as a data model, showing how these entities connect.
Types of Data Modeling
There are three primary types of data modeling, each with specific use cases:
1. Conceptual Data Modeling
• Purpose: High-level overview of the system. Focuses on what data is stored, not how.
• Audience: Business stakeholders.
• Example:
• Entities: Customer, Product, Order.
• Relationships:
• A Customer can place multiple Orders.
• An Order includes multiple Products.
Diagram Example:
Customer ─── places ───> Order
Order ─── includes ───> Product
2. Logical Data Modeling
• Purpose: Defines the structure of the data in detail, including attributes and relationships, but without database-specific implementation details.
• Audience: Data architects and analysts.
• Example (for the e-commerce system):
• Entity: Customer
• Attributes: CustomerID (PK), Name, Email.
• Entity: Order
• Attributes: OrderID (PK), OrderDate, CustomerID (FK).
• Entity: Product
• Attributes: ProductID (PK), Name, Price.
Diagram Example (ERD - Entity Relationship Diagram):
Customer (CustomerID, Name, Email)
|
└───<places>───┐
|
Order (OrderID, OrderDate, CustomerID)
|
└───<includes>───>
Product (ProductID, Name, Price)
3. Physical Data Modeling
• Purpose: Implementation-specific; focuses on how the data will be physically stored in a database.
• Audience: Database administrators and developers.
• Example (for a relational database):
• Table: Customer
• Columns: CustomerID (Primary Key), Name (VARCHAR), Email (VARCHAR).
• Table: Order
• Columns: OrderID (Primary Key), OrderDate (DATE), CustomerID (Foreign Key).
• Table: Product
• Columns: ProductID (Primary Key), Name (VARCHAR), Price (DECIMAL).
Diagram Example (SQL Schema):
CREATE TABLE Customer (
CustomerID INT PRIMARY KEY,
Name VARCHAR(50),
Email VARCHAR(100)
);
CREATE TABLE Order (
OrderID INT PRIMARY KEY,
OrderDate DATE,
CustomerID INT,
FOREIGN KEY (CustomerID) REFERENCES Customer(CustomerID)
);
CREATE TABLE Product (
ProductID INT PRIMARY KEY,
Name VARCHAR(50),
Price DECIMAL(10, 2)
);
Summary of Types
Type Focus Audience Example
Conceptual High-level entities and relationships Business stakeholders Customer places Orders with Products
Logical Attributes, relationships, no tech details Data architects/analysts ERD with attributes (CustomerID, etc.)
Physical Database implementation details DBAs/developers SQL tables and constraints