Simple Guide to Different Types of Data Modeling Techniques
Data modeling techniques help in designing how data should be structured, stored, and accessed. Choosing the right technique can improve performance, reduce errors, and make your database easy to manage.
Here are the most common types of data modeling techniques used in the industry.
1. Entity-Relationship Model – A Popular Type of Data Modeling Technique for Beginner
What is the Entity-Relationship (ER) Model?
The Entity-Relationship (ER) Model is a simple and visual way to design a database. It helps you understand how different types of data (called entities) are related to each other.
This model uses diagrams, known as ER diagrams, to show
Entities (things like Customer, Product, Order)
Attributes (details like Name, Email, Price)
Relationships (connections like "Customer places Order")
Because it’s easy to visualize and understand, the ER model is widely used by beginners when learning about data modeling.
Key Components of the ER Model
Entity: A real-world object (e.g., Student, Book, Employee)
Attribute: A property of an entity (e.g., Name, Age, Salary)
Relationship: How entities are connected (e.g., "Student borrows Book")
Example
Let’s say you're designing a library database.
Entities
Student
Book
Attributes
Student → ID, Name, Email
Book → ID, Title, Author
Relationship
A Student borrows a Book
This relationship is shown in an ER diagram to help visualize how the data connects.(Snowflake Training in Hyderabad)
Why Use the ER Model?
Visual representation – Easier to plan your database
Beginner-friendly – Great for learning database design
Foundational – Used before creating logical and physical models
Clear understanding – Shows how data items are linked
Best For
Students learning databases
Small projects and initial planning
Teams needing to communicate data structure clearly
2.Relational Data Modeling Technique – Best for Structured Data and SQL Databases
What is Relational Data Modeling?
Relational data modeling is a technique used to design structured data in the form of tables. It is the most common method used in SQL databases such as MySQL, PostgreSQL, Oracle, and Microsoft SQL Server.
In this model, data is stored in rows and columns, and each table represents an entity (like a customer, order, or product). These tables can be connected to each other using keys—making it easy to manage relationships between data.
Why is it Called “Relational?
Because the model focuses on relationships between tables. Instead of storing all data in one place, it is split into multiple related tables. These tables are connected using:
- Primary Key – A unique identifier in one table
- Foreign Key – A reference to a primary key in another table
This makes the data organized, consistent, and easy to retrieve.
Key Features of Relational Data Modeling
- Data stored in tables (also called relations)
- Each table has rows (records) and columns (fields)
- Supports Structured Query Language (SQL) for queries
- Uses keys to manage relationships
- Ideal for structured data (like names, dates, prices)
Real-World Example
Let’s say you are managing data for an online store. You can use relational modeling like this
Tables
- Customers Table
CustomerID | Name | Email
- Orders Table:
OrderID | OrderDate | CustomerID
Here, CustomerID in the Orders table is a foreign key that links back to the Customers table.
This design allows you to
- Store customer details only once
- Connect multiple orders to one customer
- Retrieve any order along with customer details using SQL
Benefits of Relational Data Modeling:
- Clear structure – Easy to understand and manage
- Data integrity – Reduces duplication and errors
- Powerful querying – Use SQL to get insights from data
- Security and control – Limit access to specific tables or data
- Data consistency – Relationships ensure updates reflect across tables
Where is It Best Used?
Relational modeling is best for structured data that follows a fixed format. It works well when:
- Data can be divided into tables
- You need clear relationships (e.g., customer → orders)
- You want to use SQL for reporting or data analysis
Common Tools and Databases
- MySQL
- PostgreSQL
- Oracle Database
- Microsoft SQL Server
- SQLite
3. Hierarchical Data Modeling Technique – Tree-Like Structure for Parent-Child Data
What is Hierarchical Data Modeling?
The Hierarchical Data Modeling technique organizes data in a tree-like structure, where each record has a parent-child relationship. One parent can have many children, but each child has only one parent.
This model is like a family tree. It's used when data naturally fits into levels or categories, such as folders in a computer or departments in a company.
It was one of the earliest data models, used in systems like IBM’s IMS (Information Management System).
Structure
- Top level: Root (or Parent)
- Lower levels: Children (or Sub-records)
- Data flows from top to bottom
Example
Imagine a company structure
Company (Parent)
HR Department (Child)
Employees in HR (Sub-child)
Sales Department (Child)
Employees in Sales (Sub-child)
In this case
- Company is the root node (parent)
- Departments are child nodes
- Employees under each department are sub-child nodes
Real-Life Examples
- File System in a Computer
- Folder (Parent) → Sub-folder → Files (Children)
- Folder (Parent) → Sub-folder → Files (Children)
- Organization Chart
- CEO (Parent) → Manager → Employees
- CEO (Parent) → Manager → Employees
- School Structure
- School (Parent) → Classes → Students
- School (Parent) → Classes → Students
Key Features
- One-to-many relationships (parent to multiple children)
- Fast data retrieval when the structure is known
- Not flexible – difficult to handle complex or many-to-many relationships
Best Used For
- Data that has a clear hierarchy or levels
- Systems where data rarely changes structure
- Applications like directory services, organization charts, file systems
4.Network Data Modeling Technique – Flexible Design for Complex Relationships
What is Network Data Modeling?
The Network Data Modeling technique is used to represent complex relationships in a flexible way. Unlike the hierarchical model (which follows a strict tree structure), the network model allows each record (data item) to have multiple parent and child records.
This model is useful when the data has many-to-many relationships and needs to be accessed from different directions.
Key Characteristics of Network Data Modeling
- Data is organized using records (like tables)
- Records are linked together through sets (connections)
- One record can be linked to multiple parents and children
- It’s based on a graph structure, not a tree
- Ideal for systems with complex data relationships
Simple Example
Imagine a university system:
- A student can enroll in many courses
- A course can have many students
This is a many-to-many relationship. In a network model, both students and courses can be connected directly, allowing smooth navigation between them.
Where Network Models Are Used
- Banking systems (customers with multiple accounts)
- Telecommunications (users with multiple plans)
- Airline reservation systems (flights with many passengers and routes)
- Manufacturing and inventory systems
Advantages of Network Data Modeling
- Handles many-to-many relationships efficiently
- Provides faster data access through multiple paths
- Offers more flexibility than hierarchical models
Limitations of Network Data Modeling
- More complex to design and maintain
- Requires careful planning of links and relationships
- Not widely used today compared to relational models
5.Object-Oriented Data Modeling Technique – Combining Programming and Data Design
What is Object-Oriented Data Modeling?
The Object-Oriented Data Modeling (OODM) technique combines the concepts of programming (especially object-oriented programming) with data modeling. It represents data as objects, just like in programming languages such as Java, C++, or Python.
In this model, both data and the operations (methods) that can be performed on the data are stored together in an object. This makes the data model more natural and reusable in software applications.
Key Characteristics of Object-Oriented Data Modeling
- Data is organized as objects, not just rows and tables
- Each object contains attributes (data) and methods (functions)
- Supports inheritance, encapsulation, and polymorphism
- Closely aligned with object-oriented programming principles
- Suitable for complex data structures
Simple Example
Imagine a "Car" object
- Attributes: Brand, Model, Color, Speed
- Methods: Start(), Stop(), Accelerate()
From this Car object, you can create other objects like ElectricCar or SportsCar, which inherit common features from the base Car object and add their own specific behavior.
Where Object-Oriented Models Are Used
- Software development projects using object-oriented languages
- Multimedia applications (images, audio, video)
- Computer-Aided Design (CAD) systems
- Real-time systems and simulations
- Complex data applications that require reuse and flexibility
Advantages of Object-Oriented Data Modeling
- Allows reuse of objects through inheritance
- Supports complex data types like images, videos, documents
- Makes it easier to design databases that reflect real-world entities
- Works well with object-oriented programming for smooth integration
Limitations of Object-Oriented Data Modeling
- More complex than traditional models
- May require specialized databases (Object-Oriented DBMS)
- Not suitable for all types of applications, especially those needing simple data structures
6.Dimensional Data Modeling Technique – Ideal for Data Warehousing and Reporting
What is Dimensional Data Modeling?
Dimensional Data Modeling is a technique used to organize data in a way that makes it easy to retrieve and analyze, especially for reporting and business intelligence. It is mainly used in data warehousing systems, where large amounts of historical data are stored and analyzed.
This model divides data into two main types: facts and dimensions. Facts are measurable values (like sales), and dimensions are descriptive data (like product, region, or date).
Key Characteristics of Dimensional Data Modeling
- Focuses on easy data retrieval for reports and dashboards
- Divides data into Fact Tables and Dimension Tables
- Supports OLAP (Online Analytical Processing) operations
- Used for decision-making and performance tracking
Simple Example
Let’s say you’re building a sales reporting system
- Fact Table: Contains measurable data
- Example: Sales Amount, Quantity Sold
- Example: Sales Amount, Quantity Sold
- Dimension Tables: Contain descriptive details
- Example
- Product Dimension (Product Name, Category)
- Time Dimension (Date, Month, Year)
- Store Dimension (Store Name, Location)
- Product Dimension (Product Name, Category)
- Example
This design helps you quickly answer questions like
- "How much did we sell last month in each region?"
- "Which product had the highest sales this year?"
Where Dimensional Models Are Used
- Data warehouse and business intelligence systems
- Sales and marketing analysis
- Financial reporting and forecasting
- Customer behavior tracking
- Any system that requires data summary and trends
Advantages of Dimensional Data Modeling
- Simple and understandable structure for end-users
- Supports fast querying and data analysis
- Helps create clear dashboards and reports
- Enables trend analysis and decision making
Limitations of Dimensional Data Modeling
- Not ideal for transactional systems (like banking apps)
- May involve data duplication for easier reporting
Needs proper ETL (Extract, Transform, Load) processes