GuidesWhen to Use NoSQL Databases: NoSQL vs. SQL

When to Use NoSQL Databases: NoSQL vs. SQL

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NoSQL databases are widely recognized for their functionality, ease of development, and scalability. They are increasingly used in big data and real-time web applications, thanks to their speed and flexibility in handling large amounts of data.

But that doesn’t mean switching to NoSQL is always the right choice. Some databases are designed for small information and quick reads, while others are suitable for larger collections of data and faster processing times.

This article will cover the difference between SQL and NoSQL databases, why NoSQL was created, and how to decide which one is best for your needs.

What is an SQL database?

A structured query language (SQL) stores and processes data in relational databases. Various attributes and their relationships are defined in rows and columns of relational databases.

In the 1970s, IBM developed SQL to connect to its System R database system. Throughout the digital age, the SQL database has been the workhorse of the backend enterprise.

A relational database uses SQL for creating, storing, updating, and retrieving data. MySQL, Oracle, and Sybase are all relational database management systems (RDBMS) that use SQL as their core programming language.

However, SQL databases lack scalability and flexibility, making them unsuitable for real-time processing and big data analytics.

What is a NoSQL database?

NoSQL databases are a new database management system class that does not use the relational model. They were developed to handle the increasingly large and complex data sets many modern businesses face.

In NoSQL, data is stored in flexible schemas designed for horizontal scalability, as opposed to the tables with rows and columns in the relational model. These schemas include JSON documents, key-value stores, wide-column databases, or graph databases.

NoSQL vs. SQL Databases compared

SQL and NoSQL databases have some fundamental differences, such as their structure, schema, and scalability.

Structure Structured Unstructured
Schema Fixed Dynamic
Scalability Vertical Horizontal
Queries Complex queries using JOIN Unsupported
Database transactions ACID BASE


SQL databases are relational databases that use structured tables and JOINs to work with data. They’re slower than NoSQL databases because they have to convert each query into multiple queries against different tables before they can return results. Even so, they are much easier to understand and maintain.

NoSQL databases are not really “no” SQL, but “not only” SQL. In other words, NoSQL may support SQL-like languages or sit alongside SQL databases, but it uses different data models and query languages.

For example, a document-based NoSQL database that uses JSON-like documents with dynamic schemas can contain any number of fields, so there’s no need for tables or JOINs when working with data. This makes it very fast to query and easy to scale horizontally, but it can also be difficult to sort through if you’re used to the simple structure of SQL tables.


NoSQL databases offer a schema-less or dynamic schema approach, allowing developers to store data without defining a strict structure beforehand. Adding new fields is easier without breaking existing code.

This flexibility enables handling complex and evolving data models more efficiently than traditional relational databases. However, querying and analyzing unstructured data is difficult.

Tables in SQL databases must adhere to rigid schemas. This allows easy queries of simple relational data, but breaks down when needing to retrieve large amounts of complex data.


NoSQL databases scale horizontally by adding more nodes rather than vertically by adding more resources (RAM and CPU) to a single node in a cluster. This means they can distribute data across multiple servers seamlessly. In high-traffic applications and with large volumes of data, this feature improves performance and availability.

In contrast, SQL databases scale vertically by adding more memory and processing power to individual nodes in a cluster. This makes them superior for large structured data sets where every piece of information needs fast access speeds.


As its name implies, SQL databases use structured query language coupled with JOIN operations to efficiently process incoming queries of any size and complexity—as long as they follow the predetermined structure.

NoSQL does not support JOINs, since its data isn’t organized into tables. Instead it offers a variety of query languages and methods tailored to the specific data model being used. When working with complex data structures, this flexibility allows developers to access, manipulate, and analyze data more efficiently than traditional SQL queries.

Database transactions: ACID vs. BASE

Another significant difference between SQL and NoSQL databases is their approach to database transactions. SQL databases use the ACID (Atomicity, Consistency, Isolation, Durability) model, which ensures strong transactional consistency and integrity.

In contrast, NoSQL databases use the BASE (Basically Available, Soft-state, Eventual consistency) model, prioritizing availability and partition tolerance over strong consistency.

Why was NoSQL created?

NoSQL databases were created in response to the massive amounts of unstructured data generated by modern applications.

As businesses accumulated larger datasets more rapidly, structured data and relational schemas didn’t always fit. It became necessary to use unstructured data and large-scale objects to better capture this information.

The response time of any RDBMS can become slow when dealing with massive amounts of data. To resolve this, these information systems can be “scaled up” by upgrading existing hardware, which is very expensive. NoSQL scales out better and is more cost-effective.

NoSQL is extremely useful for unstructured or very large data objects such as chat log data, video, or images. That’s why NoSQL became particularly popular with internet giants like Microsoft, Google, Amazon, and Meta.

When to use NoSQL vs. SQL

Choosing NoSQL or SQL for a specific project depends on the application’s requirements, data structure, and scalability needs. The following is a breakdown of when each should be used:

When to use NoSQL

You can use NoSQL for the following use cases:

  • Big data analytics: NoSQL databases efficiently handle the massive scale and variety of data generated in big data analytics, providing fast querying and processing capabilities for insights and decision-making.
  • Content management systems (CMS): NoSQL databases offer the flexibility and adaptability needed for managing diverse content types and metadata in content management systems, ensuring efficient storage and retrieval of multimedia assets.
  • Social media platforms: The ability to manage unstructured data and complex relationships in NoSQL databases make them ideal for powering social media platforms, where user-generated content, connections, and interactions continuously evolve.
  • Internet of things (IoT) applications: NoSQL databases can store and process IoT data efficiently, ensuring real-time insights and responsiveness.
  • Real-time applications: With their high write and read performance, NoSQL databases support real-time applications that require immediate data processing and analysis, such as instant messaging, gaming, or live streaming services.

When to use SQL

SQL may still be superior for the following use cases:

  • E-commerce platforms: SQL databases provide the structure, consistency, and reliability needed for e-commerce platforms to handle high volumes of transactional data, product catalogs, and customer information, ensuring data accuracy and security.
  • Human resources management systems: SQL databases present the necessary data integrity and consistency to manage sensitive employee data, such as pay and benefits.
  • Customer relationship management (CRM) systems: SQL databases are ideal for managing customer data, such as contacts, leads, and sales, allowing for efficient querying and analysis of data to gain valuable insights and improve customer interactions.
  • Supply chain management systems: SQL databases enable efficient tracking and management of supply chain data, including inventory levels, shipments, and supplier information, ensuring timely and accurate delivery of products and services.
  • Inventory management systems: With their robust querying capabilities, SQL databases provide the necessary structure and consistency to manage inventory data, such as stock levels, product details, and order tracking, ensuring efficient operations and cost-effective inventory management.

Bottom line: NoSQL databases solve some—but not all—modern problems

Many modern data management issues cannot be handled by standard SQL databases. Because NoSQL databases are flexible, scalable, and capable of managing enormous amounts of unstructured data, they are ideal for applications like social networking, e-commerce, and IoT.

Long story short, NoSQL provides cost-efficient storage, flexibility, and scalability in data stores.

Even with all its advantages, though, SQL still has the upper hand when it comes to structured data needs like inventory, personnel, and CRM systems.

Ultimately, choosing the right database solution depends on your business needs.

Al Mahmud Al Mamun contributed to this guide.

Get more information about when to scale up versus scale out in your data center.

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