Data Models in DBMS

Types of Relational Models

Data models in DBMS provide a framework for understanding, organizing, and structuring data. These models define how data is connected, stored, and manipulated. They are essential for designing efficient and meaningful databases that align with business requirements. The three primary types of data models are:

• Conceptual Data Model
   
• Representational (Logical) Data Model
   
• Physical Data Model

Conceptual Data Model

The Conceptual Data Model is the highest level of abstraction in database design. It is primarily concerned with understanding and defining what data needs to be stored in the system without worrying about how it will be implemented. This model is used during the early stages of system design to capture the data requirements of the business stakeholders.

A key example of this model is the Entity-Relationship Model (ER Model), which uses visual diagrams to represent entities, attributes, and relationships among data. It helps database architects and non-technical users communicate effectively about data structures.

Key Components of Conceptual Data Model:

• Entities: Represent real-world objects or concepts such as "Customer," "Order," or "Product."
   
• Attributes: Properties or characteristics of an entity. For example, a Customer may have attributes like Name, Email, and Address.
   
• Relationships: Logical connections between entities. For example, a "Customer places an Order" represents a relationship.

Characteristics of Conceptual Data Model:

• Provides a high-level view of organizational data.
   
• Independent of any database management system or physical considerations.
   
• Used for requirement gathering and stakeholder discussions.
   
• Often represented using ER diagrams.
   
• Helps identify business rules and constraints.
   
• Facilitates better understanding among technical and non-technical teams.

This model does not specify data types or storage mechanisms but serves as a blueprint for further data modeling stages.

Representational Data Model

The Representational Data Model, also known as the Logical Data Model, translates the conceptual model into a more structured form that can be implemented using a DBMS. It is closer to how data will be stored but still independent of the specific technology or platform.

The most common example of a representational model is the Relational Model, which represents data as tables (relations) composed of rows (tuples) and columns (attributes). It uses mathematical concepts such as Relational Algebra and Relational Calculus to manipulate data.

Role in Database Design:

• Focuses on how data should be organized logically.
   
• Defines data types, constraints, and relationships in more detail than the conceptual model.
   
• Helps database designers understand data structure clearly before implementation.

Characteristics of the Representational Data Model:

• Represents data using structured tables.
   
• Ensures data normalization and eliminates redundancy.
   
• Supports powerful query operations using SQL.
   
• Implements primary keys, foreign keys, and constraints.
   
• Forms the basis for relational DBMS like MySQL, PostgreSQL, and Oracle.
   
• Enables logical consistency and integrity of data.
   

This model acts as a bridge between the conceptual design and its actual implementation in the physical environment.

Physical Data Model

The Physical Data Model is the most detailed representation of how data is stored in the database system. It focuses on the physical aspects of storage such as file structures, indexing, data partitioning, and access paths.

This model implements the representational model by translating logical data structures into actual database files using SQL and storage configurations. It is tightly coupled with the DBMS, the operating system, and the hardware architecture.

Role in Database Implementation:

• Describes how data is stored and retrieved from the disk.
   
• Involves creation of tables, indexes, and storage allocations.
   
• Optimizes query performance through storage techniques like clustering and indexing.
   

Characteristics of Physical Data Model:

• Database dependent; tailored to a specific DBMS like Oracle, SQL Server, or MySQL.
   
• Includes exact data types, column sizes, and constraints.
   
• Contains details about indexing, partitioning, and access methods.
   
• Deals with optimization for performance and storage efficiency.
   
• Helps in tuning databases for faster response times and minimal resource usage.
   
• Represents backup and recovery configurations, storage engines, and security.

This model is crucial for database administrators (DBAs) to fine-tune and deploy a reliable, high-performance database system.

Entity-Relationship (ER) Data Model

The ER data model is a high-level data model. It serves as a blueprint for the actual representation of the data, which is later implemented as a database. The ER model is physically represented in the SQL. ER model describes the structure of a database with the help of a diagram, which is known as the entity-relationship diagram. The two main components of the ER model are :

• Entities
• An entity is a real-world object or thing. For example, a car, a student, a college, a course. Such things are known as entities. In the ER model, we try to define a relationship or association between two entities. For example, there can be two different entities like managers and departments. Through the ER model, we try to find an association between the managers and the department. The entities have various characteristics known as attributes.
• Relationship
• A relationship tells us how various entities are interrelated. It represents the association between two entities. For example, consider two different entities managers and departments, the relationship between them is "manages." Each manager "manages" a department. Similarly, each department will be managed by at least one manager.

Below is an example of an ER model diagram.

Object-based Data Model

The Object-based data model is also known as the object-oriented data model. The object-based data model is just an extension of the entity-relationship model with some extra notions of functions, encapsulation, and object identity. In an object-oriented data model, the data and relationship are present in a single structure called an object. A link is used to connect the objects. These objects are related with the help of the link.

Consider the example below:

There is an employee object. All the data and relationships are contained as a single unit. The attributes and methods are also stored in a single unit.

Also read - multiple granularity in dbms

Semi-structured Data Model

The semi-structured model is a modified form of the relational model. The semi-structured data model allows the data specifications at places where the individual data items of the same type may have different sets of attributes. In this model, some entities may have missing attributes while others may have an extra attribute. The semi-structured data model is represented using the Extensible Markup Language (XML).

Example:

<Employee 1>
<name>..............</name>
<age>.................</age>
<salary>.............</salary>
</employee 1>

Hierarchical Data Model

This is one of the older forms of data models. This data model is represented using a tree-like structure. Each entity has only one parent. However, it can have many children. At the top of the hierarchy, there is a single entity known as the root.

Also Read - Cardinality In DBMS

Network Data Model

The network data model is a modified version of the hierarchical model. It is represented by a graph. Unlike the hierarchical model, in the network model, there can be more than one parent entity or the root. It provides extra flexibility in representing the data. It is also easier and faster to retrieve data using this method compared to the network model. Here a child entity can have more than one parent entity, i.e., an entity can be related with multiple entities.

Example:

Recommended Topic, B+ Tree in DBMS

You can also learn about different types of DBMS in detail.

Advantages of Data Models

• Eliminates Data Redundancy
  Data models help organize data efficiently, avoiding duplicate entries.
  Example: In a relational model, customer information is stored once in a "Customer" table instead of duplicating it in every "Order".
   
• Ensures Data Consistency
  By maintaining structured relationships, data remains consistent across the database.
  Example: If a customer's email is updated in one table, it reflects across all related tables.
   
• Improves Data Integrity
  Data models use constraints like primary and foreign keys to enforce rules, ensuring the data remains accurate and reliable.
  Example: Prevents assigning an order to a non-existent customer.
   
• Enhances Communication Between Stakeholders
  Models like the ER diagram allow both developers and business users to understand data flow easily.
  Example: A sales team can better visualize how customer data connects to sales orders.
   
• Facilitates Better Database Design
  A good data model leads to a well-structured database that’s scalable and easy to manage.
  Example: During planning, using a conceptual data model helps map out relationships before implementation.
   
• Improves Query Performance
  Structured indexing and normalization guided by the model can optimize how queries are executed.
  Example: Indexing on foreign keys improves join performance.
   
• Simplifies Maintenance and Updates
  With a clear schema, updating or modifying the database becomes more predictable and less error-prone.
  Example: Adding a new field to a table doesn't require rewriting multiple application modules.

Disadvantages of Data Models

• Complexity in Initial Design
  Building a comprehensive data model takes time and a deep understanding of business requirements.
  Example: A retail system with hundreds of tables may take months just for modeling.
   
• Difficult to Implement Changes
  Any major structural changes may require updates across the entire application.
  Example: Changing a customer ID format may affect order, shipping, and invoice tables.
   
• Limited Flexibility for Unstructured Data
  Traditional relational models struggle with semi-structured or unstructured data.
  Example: Storing user comments or logs may be inefficient in a structured schema.
   
• Requires Skilled Professionals
  Designing an effective data model demands expertise, which can increase project cost.
  Example: Hiring experienced data architects for enterprise systems can be expensive.
   
• Performance Overhead in Complex Models
  Highly normalized models can lead to multiple joins and slow performance.
  Example: Fetching customer order details across 10 joined tables can delay response times.
   
• Tool and Platform Dependency
  Some advanced models rely on specific tools or DBMS features, making migration harder.
  Example: Moving from Oracle to MySQL may break stored procedures tied to data models.
   
• Frequent Updates Can Be Costly
  Constantly evolving business needs might demand frequent model updates, increasing workload.
  Example: An e-commerce business adding new product types needs regular schema changes.

Frequently Asked Questions

What is a data model?

The data model gives us the conceptual idea of how the data will be stored, manipulated in the database and how the final system will look after its implementation.

What are the various data models?

Relational model, ER model, Object-oriented model, semi-structured model, hierarchical model, network model.

Which data structure is used in hierarchical data models?

Tree data structure.

How is the relational data model represented?

In the form of tables having rows and columns.

Which data structure is used in a network data model?

Graphs.

Take this awesome course from coding ninjas.

Conclusion

Data models provide a clear conceptual framework for organizing and managing data in databases. They define how data is stored, accessed, and related, with types like the relational, ER, object-oriented, semi-structured, hierarchical, and network models serving different use cases. Understanding these models is essential for effective database design and implementation, ensuring data integrity, flexibility, and efficiency.