At their core, databases are simply a collection of data. However, how that data is compiled and retrieved can differ significantly from one database to another. A decision you'll need to make when choosing a modern database is whether to go with a relational (SQL) or non-relational (NoSQL) approach—the two overarching types of databases used to store and retrieve data. In this article, we will take an in-depth look at the key differences between them so that you can better understand when to choose one over the other.

Relational (SQL) Databases

As the name implies, relational databases are organized into relations. SQL databases are based on a structured query language and are used to store and manage data in a structured format, typically in tables with rows and columns. Relationships can be defined between different tables, enabling related data to be queried from multiple tables with a single statement. One of the main benefits of relational databases is that they allow you to store and manage large amounts of structured data in a way that is easy to query and update. They also support transactions—allowing multiple operations to be performed as a single unit of work, ensuring data consistency and accuracy. For example, in a customer database, there might be separate tables for customers, orders, and products. By defining relationships between these tables, we can easily answer questions like “What products did customer X order?” without querying each table separately.

Non-Relational (NO SQL) Databases

In contrast to relational databases, NoSQL databases are non-tabular and generally don't support relationships between data items. Instead of being organized into tables, NoSQL databases store data in collections (similar to JSON documents). They are designed to handle a variety of data types and structures and are often used for storing and retrieving large amounts of data that may not fit well in a traditional table-based structure (such as tweets or comments). Document-oriented NoSQL databases also have the advantage of being schema-less, which means that new fields can be added to documents without modifying the existing structure. This makes them much more flexible than relational databases, which require a predefined schema.

Key differences between SQL and NoSQL databases

In general, SQL databases are better suited for traditional, structured data, while NoSQL databases excel at handling large volumes of unstructured or semi-structured data. The choice between them depends on your application’s needs and the type of data you work with. It's important to carefully evaluate the trade-offs between the two to determine which one best fits your use case. Now, let’s look at how they differ in terms of structure, scale, cost, security, and flexibility.

Structure

Relational databases are structured in tables composed of rows and columns. SQL is used to communicate with these tables. SQL is one of the most versatile and widely used query languages available, making it a safe choice for many use cases and complex queries. However, SQL databases require predefined schemas, meaning that all data must follow the same structure. This makes the setup process complex and time-consuming, and changes later on can be disruptive.

Non-relational databases are designed for unstructured data and do not require a schema to be defined before storing data. They are ideal for handling large volumes of unstructured or semi-structured data and support rapid development of modern applications that need flexible, scalable storage. They can be accessed using various programming languages—not solely SQL. In practice, non-relational databases come in several types:

  • Document databases store information as self-contained documents (e.g., JSON or XML). Examples include MongoDB and Couchbase.
  • Key-value stores store data as records identified by unique keys. Examples include Redis and DynamoDB.
  • Wide-column databases store data in columns rather than rows, offering flexibility in naming and formatting. Examples include Cassandra and HBase.
  • Graph databases store data points in a graph structure, which is useful for identifying relationships and patterns. Examples include Neo4j and Titan.

Scale

Most SQL databases scale vertically by increasing the processing power of existing hardware (e.g., adding more RAM, SSD, or CPU). This means scaling requires more powerful—and often more expensive—hardware.

The real advantage of NoSQL databases is horizontal scaling (sharding), where a single logical dataset is split and stored across multiple machines. This distributes data across several nodes and is better suited for large volumes of data, as NoSQL systems are often designed with horizontal scaling in mind.

Cost

NoSQL databases often require less hardware and infrastructure than SQL databases, making them cheaper to maintain over time. Additionally, since NoSQL databases don’t enforce a rigid relational format, they can be more efficient in their use of disk space and memory. However, the lack of standardization in NoSQL systems may mean additional development time compared to SQL databases.

Security

NoSQL databases are often considered more secure than SQL databases because they are designed to be distributed across multiple servers, making them harder to attack. They typically use encryption to protect data from unauthorized access and are built to be self-healing, detecting and repairing corrupted data. That said, both SQL and NoSQL databases offer robust security features like encryption, access controls, and authentication.

Flexibility

NoSQL databases offer greater flexibility by allowing different types of data to be stored in one place. This makes them ideal for applications that require complex data structures. They are also designed for rapid scaling, making them suitable for large-scale projects.

Performance

The performance of a database depends on factors such as hardware, the specific database software, and the type and volume of data. In general, NoSQL databases often offer faster performance than SQL databases due to their simpler design and the ability to access data without adhering to a relational format. Additionally, their distributed nature allows them to utilize multiple processors and machines, further enhancing performance. However, SQL databases may outperform NoSQL systems in certain types of queries.

SQL vs NoSQL: When to use

There isn’t a one-size-fits-all solution when it comes to databases. Many businesses rely on both relational and non-relational databases for different tasks. Generally, if your data is structured, you need ACID compliance (Atomicity, Consistency, Isolation, Durability), and your data doesn’t change much over time, a relational database is likely the better choice. However, if your data is unstructured or if you require high scalability and availability to handle large volumes of traffic, then NoSQL might be preferable. Businesses dealing with large datasets—such as user profiles, web analytics, and financial transactions—should consider NoSQL databases for scenarios where speed and scalability are critical.

Top 4 NoSQL Databases

  • MongoDB: A document-oriented NoSQL database used for high-volume data storage. It uses JSON-like documents with dynamic schemas, simplifying data integration.
  • Cassandra: A distributed NoSQL database system that is highly scalable and designed to manage very large amounts of structured data. It offers high availability with no single point of failure.
  • DynamoDB: A fully managed NoSQL database by Amazon Web Services. It delivers high performance and scalability with minimal administration and no downtime, featuring a flexible data model and built-in security.
  • HBase: An open-source, distributed NoSQL database that runs on the Apache Hadoop file system. It provides random real-time read/write access to big data and is well suited for low-latency applications.

Top 4 SQL Databases

  • PostgreSQL: An open-source database management system known for its high performance, reliability, and flexibility. It is widely used in web development.
  • MySQL: A popular open-source database management system noted for its high performance and ease of use, with a large user community.
  • Microsoft SQL Server: A commercial database management system developed by Microsoft. It is known for its performance, security, and scalability, and offers a wide range of features for data management and analysis.
  • Oracle: A widely used commercial database management system recognized for its performance, scalability, and robust security features.