There are various ways for storing and managing information in a database. Each of the operations in a database is designed for a specific requirement. Relational and non-relational databases both store information, but their structures and functionalities have major differences. Understanding these differences between relational and non-relational databases is important when working with data, whether you are a beginner or an experienced individual. In this article, we will learn about the differences between relational and non-relational databases, their advantages and disadvantages, and where they should be used in detail.
Table of Contents:
What is a Relational Database?
A relational database is a system that keeps data in structured tables. Each table consists of rows and columns. You can think of a table like a spreadsheet, where each row represents a record and each column a field. These tables will often relate to one another and help to organize and retrieve data efficiently.
Relational databases provide a fixed schema. This means you design the structure of the data before records are added. This helps ensure data consistency and accuracy, especially helpful in the systems used for financial data, customer data/CRM-type data, and transactional data.
What is a Non-Relational Database?
A non-relational database can store data, for example, as documents, key-value pairs, graphs, or wide columns, and has a more flexible format than a relational database. A non-relational database is designed for larger volumes of various types of data, even a mixture of types, and should not be concerned with strict organization.
Non-relational databases use dynamic or schema-less models, which means the way its data is stored can change over time. This allows non-relational databases to be optimal for application development environments that need to expand, adapt, and change quickly, as with content management systems or IoT applications.
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Difference Between Relational and Non-Relational Databases
Here is a clear comparison to provide the key differences between relational vs non-relational databases:
| Feature |
Relational Databases |
Non-Relational Databases |
| Data Structure and Storage |
Structured data stored in tables with rows and columns. |
Data stored as documents, key-value pairs, graphs, or wide-columns. |
| Schema Flexibility |
Uses a fixed schema defined before data is added. |
Supports flexible or schema-less data models. |
| Scalability and Performance |
Scales vertically by upgrading the server. |
Scales horizontally by adding more servers. |
| Data Integrity and Consistency |
Follows strong consistency using ACID rules. |
Uses eventual consistency and is more flexible. |
| Query Language |
Uses SQL for managing and querying data. |
Uses different query methods depending on the database type. |
| Best Use Cases |
Good for complex queries and structured data like banking systems. |
Good for big data, content management, or IoT applications. |
| Examples |
MySQL, PostgreSQL, Oracle |
MongoDB, Cassandra, Redis |
Knowing and understanding the primary differences between relational and non-relational databases will help to select the best tool for the appropriate task.
Advantages and Disadvantages of Relational Database
Below are the advantages and disadvantages of relational databases.
Advantages
- Data Integrity and Accuracy: Relational databases use rules like unique keys and constraints to ensure data remains clean, correct, and reliable.
- Strong Support for Complex Queries: SQL makes it easy to perform advanced searches, filters, joins, and reports across multiple related tables.
- Great for Structured and Related Data: Best suited for use cases where the data is clearly organized and has strong relationships between tables.
- Reliable Transaction Handling: ACID compliance ensures that data transactions are consistent, reliable, and secure — ideal for systems like banking or e-commerce.
Disadvantages
- Less Flexible with Unstructured Data: You need to define the structure before adding data, making it less flexible to frequently change or handle different types of data.
- Harder to Scale out Across Multiple Servers: Scaling requires significant dollars to upgrade a single machine (vertical scaling). More limited with less consistent return on investment.
- Applications Require Careful Planning When Making Changes to the Data Schema: Making table structures or changing relationships can have implications for your applications and require significant consideration and planning.
- Complex Migrations and Upgrades: Updating databases or migrating to new versions can be time-consuming and may risk data loss or service downtime.
Advantages and Disadvantages of Non-Relational Database
Below are the advantages and disadvantages of non-relational databases.
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Advantages
- Flexible Data Models: Support various formats like JSON, key-value pairs, and graphs, allowing better adaptation to different types of data.
- Easy Horizontal Scalability: Designed to scale across multiple servers, making it suitable for large-scale applications.
- Good for Modern Applications: Works well with dynamic, real-time, mobile, and web-based systems that require constant changes in data storage methods.
- Handles Unstructured Data Efficiently: Efficiently handles large volumes of unstructured or semi-structured data.
Disadvantages
- Weaker Consistency Guarantees: Some systems follow eventual consistency, prioritizing speed and availability over strict data accuracy.
- Lack of Standard Query Language: Each system often uses a different query language, making it harder to learn or switch between databases.
- Complexity in Data Relationships: Managing complex joins or relationships between data can be more difficult compared to relational databases.
- Limited Tooling and Support: Fewer mature tools and ecosystem support than relational databases, especially for reporting or analytics.
Popular Relational Databases
Some examples of relational databases
1. MySQL
MySQL, the most popular open-source relational database, used primarily in web-based applications, is known for its strict standardization, simplicity, and support of developers across many different contributions.
2. PostgreSQL
PostgreSQL is an advanced open-source database supporting complex queries, including custom functions, ideal for growing applications requiring strict data integrity as well as performance.
3. SQL Server
SQL Server is an enterprise-quality database solution available from Microsoft, designed for deep integration and use in Windows environments, with extensive business intelligence and data management tools.
4. Oracle Database
Oracle database is a relational database designed for high-performance and scalable enterprise applications. It possesses sophisticated security, analytics, and automation features.
Popular Non-Relational Databases
1. MongoDB
MongoDB is a document-oriented database that stores data as JSON-like documents, and is essential for modern applications that utilize dynamic and unstructured data.
2. Redis
An in-memory key-value database used for caching, real-time analytics, and processing data quickly, and is recognized for speed and lightweight data processing.
3. Cassandra
Cassandra is designed for massive amounts of data in a distributed system. Cassandra is extremely resilient with multiple available nodes providing consistent availability and fault tolerance for applications needing a massive data handling capability.
4. Neo4j
A graph database designed to handle complex relationships, and is commonly used in recommendation engines, fraud detection, and network analysis.
All of these databases allow for many varied use cases, but are built in a way that allows for fast and flexible data manipulation.
When to Use Relational vs Non-Relational Database?
Use relational or non-relational databases only when required, based on the data use case.
Use a Relational Database When:
1. Your data is structured and does not frequently change: Ideal for situations where data types and relationships are well defined and remain constant over time.
2. You require a high degree of accuracy and consistency: Relational databases enforce very strict rules to maintain the integrity of the data, which is especially important for financial or legal data.
3. When complex queries and reporting will be used: You can perform advanced SQL queries to extract, join, and analyze related data easily across multiple tables.
4. Basically, systems for banking, inventory, and HR applications: These applications depend on transactional safety, structured data, and detailed reporting, which relational databases handle effectively.
Use a Non-Relational Database When:
1. When dealing with varied or unstructured data: Useful when you have data formats such as text, images, logs, or any user-generated content that cannot be readily stored in a table.
2. When you need to scale rapidly because of usage: Non-relational systems are designed to scale horizontally, which is useful because it is easy to add more servers to meet the demand if needed.
3. When real-time access and performance are the main concern: Non-relational systems are great for a very fast read/write environment, like messaging applications or live dashboards.
4. Projects like digital systems for social media, big data platforms, and content delivery networks: These types of systems will probably find themselves managing large data loads, but most importantly, they are unpredictable.
Deciding between relational and non-relational databases will be based on your application requirements, like their structure, control over the data, or flexible growth.
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Conclusion
Whether to choose relational or non-relational databases depends on the kind of data you are dealing with and your application requirements. If you have legacy data, structured, or consistent data with complex relationships, relational is favorable. If you have unstructured data or data that is growing quickly or at scale, non-relational is more favorable for your application. Having a basic understanding of the differences allows you to make better architectural decisions.
To learn more about databases, check out this SQL Course and also explore Database Interview Questions prepared by industry experts.
Relational Database vs Non-Relational Database – FAQs
Q1. What is the main difference between relational and non-relational databases?
Relational databases use fixed tables and structure, while non-relational store flexible data types.
Q2. Which is better for unstructured or mixed data formats?
Non-relational databases are better for storing unstructured or mixed types of data easily.
Q3. Can non-relational databases do joins like relational ones?
They can perform joins, but it’s more complex and slower than in relational databases.
Q4. Do relational databases follow ACID rules for data safety?
Yes, relational databases follow strict ACID rules to keep data safe and correct.
Q5. When should I choose a non-relational database over relational?
Use non-relational when your data grows fast or changes often with no fixed format.