Relational Database vs NoSQL: What You Need To Know

Choosing the most suitable database system is crucial. It enables efficient data management, scalability, and performance. With many options available, your decision boils down to selecting a traditional relational database or a NoSQL database.

Both have their strengths and weaknesses, making them suitable for different use cases.

But there’s one problem: How do you choose the best database with so much information out there?

In this article, we will help you by delving into the key differences between relational and NoSQL databases, their advantages and disadvantages, and guide you on when to use each:

What Is a Relational Database?

A relational database organizes data into tables consisting of rows and columns. Each table shows a specific entity (like customers or orders), and you can define the relationships through tables.

SQL (Structured Query Language) is the standard language used to interact with relational databases. It enables you to perform key operations—such as querying, updating, and managing the data.

Key Features of Relational Databases

• Structured data model: Data is stored in tables with predefined schemas, which implement data integrity and consistency. This is critical for many professional services sectors.
• Schema-based: The structure of the data is defined by a schema, which determines the type of data you can store in each table.
• ACID compliance: Relational databases follow ACID (Atomicity, Consistency, Isolation, Durability) properties. This ensures reliable transactions.
• Scalability: Relational databases are typically vertically scalable, meaning they scale by increasing the power of a single server.

Advantages of Relational Databases

1. Data Integrity

One of the main benefits of relational databases is how they deliver a strong schema where data integrity is vital. For example, data must be accurate in healthcare applications and financial systems; otherwise, errors can be expensive for your business.

2. ACID Transactions

Relational databases adhere to ACID (Atomicity, Consistency, Isolation, Durability) principles. As a result, this ensures reliable and consistent transactions through ACID properties. This makes them suitable for applications requiring high levels of data accuracy and reliability.

3. Standardized Query Language

Relational databases use SQL—a widely known and standardized query language. This makes it easier for developers and database administrators to manage, query, and manipulate the data. Likewise, SQL makes it more straightforward to hire talent, maintain systems, and develop complex queries.

4. Mature Ecosystem

Relational databases have a long history and are supported by a robust ecosystem of tools, documentation, and community expertise. This includes wrong database management tools, such as Oracle, MySQL, PostgreSQL, and SQL Server.

5. Security 

Relational databases often provide advanced security features. These include role-based access control, encryption, and auditing capabilities. These features protect sensitive data and ensure compliance with regulatory standards like GDPR, HIPAA, and PCI-DSS.

Also, fine-grained access control ensures that only authorized users can view or modify specific data, which is essential for handling confidential information.

Disadvantages of Relational Databases

1. Scalability Limitations

Relational databases typically scale vertically, which can be expensive and less flexible compared to the horizontal scaling options offered by NoSQL databases.

2. Schema Rigidity

The fixed schema of relational databases can make it difficult to adapt to changing data requirements, as altering the schema often requires significant changes to the database structure.

3. Performance Issues

Handling large volumes of unstructured data can lead to performance bottlenecks, as relational databases are optimized for structured data.

4. Cost

Maintaining relational databases at scale can be costly, especially when considering licensing fees and the need for powerful hardware to support vertical scaling.

What is a NoSQL Database?

NoSQL databases are a category of database systems. They are designed to handle large volumes of unstructured, semi-structured, or structured data.

Unlike relational databases, NoSQL databases don’t require a predefined schema. This means they’re more flexible for data storage and accessibility.

There are several types of NoSQL databases:

• Document databases
• Key-value stores
• Column-family stores
• Graph databases. 

Each one is optimized for specific data models and use cases.

Key Features of NoSQL Databases:

• Flexible Schema: NoSQL databases do not require a fixed schema, allowing for dynamic and evolving data models.
• Horizontal Scalability: They are designed to scale out by adding more servers, making them ideal for handling large-scale distributed systems.
• BASE Properties: NoSQL databases often follow BASE (Basically Available, Soft state, Eventual consistency) principles, allowing for more flexible data consistency models.
• Varied Data Models: Support for various data models, such as key-value pairs, documents, graphs, and wide-columns.

The Advantages of NoSQL Databases

1. Scalability

NoSQL databases are designed for horizontal scalability. This lets them manage large data quantities by adding more servers to spread the load.

Furthermore, this makes them well-suited for applications requiring scaling out as your data grows—i.e., social media platforms and big data analytics.

2. Flexibility

NoSQL databases support dynamic schemas. Consequently, this delivers flexibility for changing data needs, which is beneficial for applications where the data structure evolves. It’s also advantageous where data is semi-structured or unstructured (JSON documents or large text files).

3. High Performance

Optimized for high performance, NoSQL databases are brilliant for handling large datasets with low latency. This makes them excellent for real-time applications—i.e., online gaming, IoT, and real-time analytics—where rapid data processing is crucial.

4. Cost-Effective

NoSQL databases can be more cost-effective due to their efficient resource utilization and the availability of open-source options. They can also run on commodity hardware, reducing infrastructure costs compared to traditional relational databases, which may require more expensive hardware.

The Disadvantages of NoSQL Databases

1. Lack of Standardization

NoSQL databases lack a standardized query language like SQL, often leading to complexity in development and maintenance. Each NoSQL database may also have its query language or API, which can create a steeper learning curve for developers. It can also complicate integration with other systems.

2. Eventual Consistency

Many NoSQL databases follow BASE (Basically Available, Soft state, Eventual consistency) properties.

However, this may not guarantee immediate consistency across all nodes. So this consistency model can be a disadvantage in applications where data consistency and accuracy are critical. These include financial systems or inventory management.

3. Maturity

Some NoSQL technologies are relatively new and lack the robust tooling, documentation, and community support that relational databases offer. This can make it more challenging to find skilled personnel, troubleshoot issues, or implement best practices when using these databases.

4. Complexity in Transactions

Handling complex transactions can be more challenging in NoSQL databases compared to relational databases, which are designed to support ACID-compliant transactions. This can make NoSQL databases less suitable when you need applications to perform complex operations consistently.

Difference between Relational Databases and NoSQL

Relational databases and NoSQL databases serve different purposes and are built on fundamentally different architectures.

Relational databases are highly structured and optimized for transactional consistency, so they’re suitable for applications where data integrity and complex queries are essential.

NoSQL databases, on the other hand, offer greater flexibility, scalability, and performance, particularly when dealing with large volumes of diverse or unstructured data.

Relational databases and NoSQL databases differ in 5 key ways:

• Data Model: Relational databases use a tabular data model, while NoSQL databases use various models, including document, key-value, and graph.
• Schema: Relational databases have a predefined schema, whereas NoSQL databases allow for dynamic schemas.
• Query Language: SQL is the standard for relational databases, whereas NoSQL databases may use various query methods depending on the data model.
• Transactions and Consistency: Relational databases are ACID-compliant, ensuring strict transaction consistency. NoSQL databases often follow BASE properties, prioritizing availability and scalability.
• Scalability: Relational databases typically scale vertically, while NoSQL databases are designed for horizontal scalability.

When Should You Use Relational vs. Non-Relational Databases?

The choice between relational and NoSQL databases depends largely on the specific needs of your application:

Use Relational Databases When:

• You need to ensure data integrity and strong consistency.
• Your data model is structured and unlikely to change frequently.
• You require complex querying capabilities.
• Your application relies on ACID transactions.

Use NoSQL Databases When:

• You need to handle large volumes of unstructured or semi-structured data.
• Your application demands high availability and horizontal scalability.
• Flexibility in schema design is crucial.
• You prioritize performance and can work with eventual consistency.

Can Relational and NoSQL Databases Be Used Together?

Yes, you can use relational and NoSQL together in a complementary fashion—a strategy known as polyglot persistence. By leveraging the strengths of both types of databases, you can optimize for specific use cases.

For instance, a relational database might be used for transaction processing, whereas a NoSQL database handles large-scale analytics or real-time data processing. This allows you to choose the right tool for each task.

Here are examples where you can use them together:

1. Transactional Systems with Analytical Processing

You can use a relational database for handling transactional data with strict ACID compliance, while a NoSQL database supports large-scale data analytics and real-time processing. This is an excellent time to use them together.

2. Structured Data with Unstructured Data Management

You can combine a relational database for structured, tabular data and a NoSQL database for storing and querying unstructured or semi-structured data, including logs, multimedia, or documents.

3. High Availability with Consistent Transactions

You can leverage NoSQL databases for applications requiring high availability and distributed data while relying on a relational database for operations that need consistent transactions and data integrity.

4. E-Commerce Platforms with Product Catalogs

You can use a relational database for managing core transactional data like orders and customer records and a NoSQL database for flexible product catalogs that require rapid updates and retrievals.

5. User Authentication with Social Media Integration

You can utilize a relational database to manage secure user authentication details. In addition, a NoSQL database handles the social media interactions and feeds, which are more dynamic and unstructured.

6. Real-Time Data with Historical Reporting

You can employ a NoSQL database for real-time data ingestion and processing. Additionally, a relational database maintains historical data for detailed reporting and compliance purposes.

Final Thoughts 

Choosing between a relational database and a NoSQL database depends on your requirements for scalability, consistency, and data structure.

Relational databases excel in environments requiring structured data and strong consistency—whereas NoSQL databases offer flexibility and scalability, particularly for unstructured data.

Often, the best solution involves using both types of databases together to leverage their respective strengths. For businesses looking to streamline data integration and management, Rivery can help automate and optimize your data workflows.