Data Science

Difference Between Relational and Non Relational Database

Published: 14th November, 2023

Arpit Mehar

Content Developer Associate at almaBetter

Explore the world of databases with our insightful blog on the Difference Between Relational and Non Relational Databases. Uncover the key distinctions now!

Welcome to our comprehensive exploration of the fundamental choices in data management: the age-old debate of relational database vs Non Relational database. Understanding the nuances between these two approaches is crucial in the evolving information storage and retrieval landscape. Join us on a journey through the intricacies of relational vs Non Relational databases as we unravel their differences, advantages, and optimal use cases. Whether deciphering the significance of relational database vs. Non Relational database or simply seeking clarity on the pros and cons of Non Relational database vs relational database, our in-depth guide is designed to demystify the complexities and empower your decision-making process. Let's navigate the relational and Non Relational database terrain together, shedding light on the key considerations shaping your data strategy.

What is Relational Database and Non Relational Database

Before we understand the difference between relational databases and Non Relational databases, let’s look at the definitions and key features of both terms.

A relational and Non Relational databases are two different types of database management systems that organize and store data in distinct ways. Here's a brief overview of each:

Relational Database


  • A relational database is a database management system (DBMS) that uses a structured format to organize data.
  • It organizes data into tables, each consisting of rows and columns.
  • Relationships between tables are established through keys, which link data in one table to data in another.

Key Characteristics:

Structured Schema: Relational databases have a fixed and predefined schema specifying data types and relationships between tables.

SQL (Structured Query Language): SQL is the standard language for interacting with relational databases. It provides a robust and standardized way to define, manipulate, and query the data.

ACID Properties: Relational databases adhere to ACID (Atomicity, Consistency, Isolation, Durability) properties, ensuring transaction data integrity.

Use Cases:

  • Well-suited for applications where data relationships are clearly defined and unlikely to change frequently.
  • Commonly used in traditional business applications, financial systems, and applications with complex queries.

Non Relational Database


  • A Non Relational database, also known as NoSQL (Not Only SQL), is a type of database management system that does not rely on the traditional tabular relational model.
  • It offers more data storage and retrieval flexibility, allowing for various data models.

Key Characteristics:

  • Flexible Schema: Non Relational databases often have a dynamic or schema-less approach, allowing for more flexibility in data structure.
  • Diverse Data Models: Non Relational databases can use different data models, such as document-oriented (e.g., MongoDB), key-value pairs (e.g., Redis), wide-column stores (e.g., Cassandra), or graph databases (e.g., Neo4j).
  • Horizontal Scalability: Many Non Relational databases are designed for easy horizontal scalability, enabling the addition of more servers to handle increased data loads.

Use Cases:

  • Ideal for scenarios where data structures evolve rapidly, or scalability and flexibility are paramount.
  • Commonly used in applications with large amounts of unstructured or semi-structured data, real-time big data processing, and scenarios where rapid development and deployment are crucial.

What is the Difference Between Relational and Non Relational Database

Relational and Non Relational databases represent distinct approaches to organizing and managing data. Here are the key differences between the two:

  • Data Structure:

Relational Database: Data is structured in tables with rows and columns, creating a well-defined schema. Each table represents an entity, and relationships between entities are established through keys.

Non Relational Database: Data is often stored in various ways, such as key-value pairs, document-oriented collections, wide-column stores, or graph databases. The data structure typically has more flexibility, allowing for a schema-less or dynamic schema approach.

  • Schema:

Relational Database: Adheres to a fixed schema where the data types and structure are defined before inserting data. Changes to the schema often require altering the entire database.

Non Relational Database: Offers more flexibility, allowing dynamic changes to the data structure without affecting the entire database. This can be particularly advantageous in scenarios where data evolves rapidly.

  • Scalability:

Relational Database: Scaling vertically (adding more resources to a single server) is common. This can become expensive and may have limitations in terms of scalability.

Non Relational Database: Generally more adept at horizontal scaling (adding more servers to a distributed network), making them well-suited for handling large volumes of data and accommodating growing workloads.

  • Query Language:

Relational Database: Utilizes SQL (Structured Query Language) for defining and manipulating the data. SQL provides a powerful and standardized way to interact with relational databases.

Non Relational Database: Query languages can vary based on the type of Non Relational database. Examples include MongoDB's query language for document databases and Cassandra Query Language (CQL) for wide-column stores.

  • Use Cases:

Relational Database: Well-suited for applications where data relationships are clearly defined and unlikely to change frequently. It is commonly used in traditional business applications, financial systems, and applications with complex queries.

Non Relational Database: Ideal for scenarios with evolving and dynamic data, where scalability and flexibility are essential. Everyday use cases include content management systems, real-time big data processing, and applications with frequently changing data requirements.

Relational Database vs Non Relational Database Example

Scenario: Online Retail Store

  • Relational Database Example (e.g., MySQL, PostgreSQL):
    • Tables:
      • Customers
      • Orders
      • Products
    • Relationships:
      • Each customer has a unique ID associated with orders through a customer ID.
      • Each order is linked to specific products through a product ID.
    • Schema:
      • Customers table (customer_id, name, email)
      • Orders table (order_id, customer_id, order_date)
      • Products table (product_id, product_name, price)
    • Query:
      • A SQL query can easily retrieve information like "List all orders with customer details and products."
  • Non Relational Database Example (e.g., MongoDB - Document Store):
    • Collections (Equivalent to Tables):
      • Customers
      • Orders
      • Products
    • Documents (Equivalent to Rows, but with flexible schema):
      • Customer Document: { "_id": 1, "name": "John Doe", "email": "" }
      • Order Document: { "_id": 101, "customer_id": 1, "order_date": "2023-11-10", "products": [ {"product_id": 201, "quantity": 2}, {"product_id": 202, "quantity": 1} ] }
      • Product Document: { "_id": 201, "product_name": "Laptop", "price": 1200 }
    • No Fixed Schema:
      • The structure of documents in the Orders collection can vary; for example, different orders may contain different sets of information.
    • Query (MongoDB Query Language):

Retrieve orders along with customer details and products using a query like:

 { $lookup: { from: "Customers", localField: "customer_id", foreignField: "_id", as: "customer_details" } }


In conclusion, delving into the intricacies of database management has unveiled a world of choices, particularly in the perennial debate of "relational database vs Non Relational database." The exploration of their differences, illustrated through real-world examples, has shed light on the diverse landscapes these systems navigate.

The difference between relational and Non Relational database examples has demonstrated that while relational databases provide structured and schema-driven solutions for well-defined relationships, Non Relational databases offer flexibility and scalability, accommodating the dynamic nature of modern data.

Whether you're contemplating the rigid structure of a relational database or the adaptable schema of a Non Relational counterpart, understanding the strengths and weaknesses of each is crucial. The scenarios presented, from the structured elegance of SQL queries to the dynamic nature of NoSQL collections, underscore the importance of aligning your choice with the specific needs of your project.

As we navigate the evolving landscape of data management, the key takeaway is that there is no one-size-fits-all solution. The decision between a relational database vs. a Non Relational database hinges on factors such as data complexity, scalability requirements, and the propensity for change within your application's data model.

In essence, the journey through the difference between relational and Non Relational database examples has empowered us to make informed decisions, considering the present needs of our projects and their potential for growth and adaptation. The database paradigm you choose becomes a cornerstone in shaping your data infrastructure's efficiency, scalability, and agility. Armed with this knowledge, you are better equipped to navigate the dynamic domain of databases and architect solutions that stand the test of evolving data landscapes.

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