Relational vs. Non-relational Database: The Difference Explained

Whether you’re an aspiring data scientist or are simply in a job that requires you to use data on a daily basis, you’ve probably encountered the terms relational and non-relational databases. Although both types of databases exhibit clear differences, you may still find yourself intrigued by their specifics and applications, prompting a desire to understand them further.

Read on to delve deeper into relational and non-relational databases, view lists of common databases used today, and gain insights into selecting the optimal database solution for your needs.

What is a relational database? 

A relational database stores data in tables composed of rows and columns. 

Relational databases contain data within a table, which links to data contained within other tables through unique identifying keys. Specifically, relationships between tables form when a primary key, which uniquely identifies a row in one table, connects with a foreign key identifying a row of data in another table. 

In effect, relational databases store structured or well-defined data like names, dates, and quantities that can be easily standardised within a table. SQL, or Structured Query Language, is the most common programming language for interfacing with relational databases within relational database management systems (RDMS). 

Common relational databases

Relational databases are easy to use for storing, retrieving, and manipulating well-defined, structured data. Some of the well-known relational databases include: 

• MySQL

• IBM Db2

• Snowflake

• Amazon Aurora

• PostgreSQL

• Microsoft SQL Server

What is a non-relational database?

A non-relational database is a type of database that doesn’t store data in tables but instead in whatever format is best for the data. In effect, non-relational databases contain unstructured or loosely defined data like email messages, videos, images, and business documents that aren’t easily standardised. Users can also employ these databases to store a mix of structured and unstructured data. 

Non-relational databases are said to be NoSQL, meaning they don’t use Structured Query Language, even though many NoSQL databases support SQL queries. 

Types of non-relational databases

Unlike relational databases, which only support a tabular design, you can choose from many different types of non-relational databases. Some of the standard approaches to organising non-relational databases include:

• Key-value stores: In a key-value store, data receives a unique identifier, which allows users to retrieve and sort it. The system consists of "keys”, or unique identifiers like a string of numbers, and the "values" with which they're associated, which can be either data itself or simply its location within the database.

• Column-family data stores: This type of non-relational database organises data into a "keyspace" containing multiple families of different columns. The column families, in turn, include rows that each also contain columns containing data. Users can access and identify the data using keys.

• Graph databases: Graph databases store data in nodes and structure them based on their relationships to one another, allowing for a much more flexible schema than tabular designs facilitate.

• Document databases: Document databases store data within documents, typically containing one object and all its associated metadata.

Common non-relational databases

Some of the most common non-relational databases include: 

• MongoDB

• IBM Cloundant

• Amazon DynamoDB

• Apache Cassandra 

SQL vs. NoSQL: What’s the difference?

Structured query language (SQL) is the language used in relational databases to store structured data. NoSQL stands for “not only structured query language” and is used for non-relational databases that store structured and unstructured data.

In short, SQL databases are relational databases, and NoSQL databases are non-relational databases.

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Relational databases are optimal for managing data with predictable size, structure, and usage frequency. Conversely, non-relational models prove more effective for storing data adaptable in form or dimension or subject to future changes over time.

If you want to learn more, the Learn SQL Basics for Data Science Specialisation from UC Davis teaches beginners with no previous coding experience the basics of SQL, data wrangling, AB testing, and more.