What is a NoSQL Database? [An Ultimate Guide]

There were almost 2 billion websites in 2022 alone. As websites and platforms grow rapidly, the demand for reliable, scalable, and customizable databases becomes paramount. 

This is where NoSQL databases come into play, offering a robust data storage and management solution. IBM defines NoSQL databases as: 

In this article, you will have a deeper understanding of NoSQL databases—including what their benefits are and what sets them apart. 

Discover why a NoSQL database may just be the perfect solution for your data storage and management needs.

Understanding the Concept of a Database

Databases are designed to make data and datasets easy to access, manage, and update. The data, database management system, and associated applications are all part of the database system. 

Smaller-scale databases can often be operated from a single machine—whether for passion projects or personal websites. They can also be used by small businesses to store essential company data. 

For larger-scale databases, server clusters are often employed through physical in-house rack solutions or a cloud-based system. 

Managing data in the cloud may be easier for some organizations and offers greater redundancy for stored data.

Different Types of Databases

Database solutions are classified based on their data structure or model. They all fall into different types. Some of the most common types include:

1. Hierarchical Databases

This database uses a tree-like structure of ranks or levels to organize and arrange data systematically. 

Data is categorized based on a common point of linkage. This means that two data entities will be placed lower in the hierarchy while the commonality between them would place higher.

Another version of how hierarchical databases organizes and arranges data is through a parent-child relationship—where adding multiple data elements results in a tree-like structure. 

The child data is linked to the parent through a field, allowing multiple child records underneath them. However, with traditional hierarchical databases, the vice versa isn’t possible.

An inherent downside of a hierarchical database is that it can become complicated to navigate using larger datasets. Adding extra data elements complicates the searchability of the set.

2. Network Databases

A network database is a type of hierarchical database that uses a parent-child structure. However, one should note that it is different from traditional hierarchical databases in one way. 

In network databases, each record or file can link to multiple parent and child records and vice versa. This results in a more generalized graph-like structure.

Having a more complex framework, this type of database can better represent multi-directional relationships between multiple data points. 

However, a disadvantage of this solution is its overall complexity and how it is dependent on the structure than other solutions.

3. Object-Oriented Databases

This type is designed with object-oriented programming in mind, allowing developers to keep a consistent object model in both their applications and databases.

Information stored in this database is represented as objects like those commonly used in object-oriented programming languages.

This type of database differs from more common relational databases, which employ a table-oriented design. There is a solution known as object-relational databases, which act as a functional hybrid model of both kinds.

4. Relational Databases

This is the most common database solution in the IT field today. Relational databases store and allow access to data points related to one another. 

It’s based on the relational data model developed in 1970 by Edgar Frank Codd at IBM. Most of these systems use Structured Query Language (SQL) to query and update the database as needed.

Relational databases organize data into a table structure using rows and columns, with data typically being structured across multiple tables, which can be joined using a primary or foreign key. 

These identifiers showcase the relationships between data in these tables, and the relationships between them are represented as data models.

5. NoSQL Databases

Unlike relational databases, a NoSQL database is a non-relational database. 

As non-tabular databases, NoSQL databases are capable of storing, accessing, and managing large sets that traditional relational databases using SQL may struggle with.

NoSQL Databases: An Overview

As a non-relational database, NoSQL databases are commonly deployed to run real-time web applications and support big datasets. 

A NoSQL database can store data within a single structure, unlike tabular relational databases. Because this database design doesn’t require a schema, it can easily scale and manage large and unstructured datasets. 

Check out IBM’s simplified explanation of what a NoSQL database is in the video below: 

While they’re a reliable alternative to SQL-dependent relational databases in specific scenarios, NoSQL databases are not meant to replace the latter outright. 

Instead, they function better in specific situations—like processing large datasets. They can even be deployed alongside SQL databases. 

Developing and running a NoSQL solution also does not come cheap. Companies can expect to pay a freelance backend developer over $60 per hour to do the task.

Types, Examples, and Uses Cases of NoSQL Databases

There are several different types of NoSQL databases. Each type has its own best uses, structures, advantages, and drawbacks. 

Remember that the best database for your situation depends heavily on the type of data and what you’re trying to do with it.

Below are some of the best-known types of NoSQL databases, along with examples and use cases for each. 

Key-Value Stores

This NoSQL database is the simplest out of the available options. It is an excellent choice for situations where horizontal scaling is important (e.g., managing big data). 

Each item in the database is stored as an attribute or “key” along with its value.

A key scenario where it makes sense to use a key-value database would be session storing. A session ID acts as a key, while the associated data from the user is stored as the value.

Well-known examples of Key-Value Store databases are DynamoDB from Amazon and Redis—which is a simple but robust open-source solution.

Wide Column Stores

This NoSQL database type stores data in tables, rows, and dynamic columns for easy data access and management, similar to SQL databases.

However, a wide-column store database can feature tables with columns from each row that don’t share names or a standard format.

Furthermore, each row can have a different number of columns. You can also add a column to a row anytime without doing the same for others.

A popular use case for wide-column store databases is for real-time analytics since this database type can readily process large amounts of data and perform complicated analytics on the fly.

Two popular examples of wide-column store databases include Cassandra from Facebook and Google’s Bigtable. Both are powerful databases backed by some of the largest tech firms in the industry.

Graph Databases

Like relational databases, graph-based databases emphasize the value of relationships between data points.

While other databases associate different data with underlying connections, graph solutions use dependencies to name, handle, and assign properties to relationships. 

This type of NoSQL solution is best used on datasets that feature interconnected elements and are best represented as a graph with an undetermined amount of relationships between them.

Graph databases are also best for crafting knowledge graphs. These graphs are often used in search engines storing entities and their interrelations, allowing more context-aware search results.

A common example of a graph database is Neo4j, which is an open-source option implemented in Java. Neptune from Amazon is also a fully-managed graph database service. 

Here’s an example of how the data look like to a graph database like Neo4j:

Document Databases

Document-oriented NoSQL databases store data in documents, typically JSON, BSON, or XML. This type is better suited for nested and more complicated data structures. 

In this setup, all metadata from a particular item is stored in a single location. The database can group distinct documents in a single collection and index them using their identifier and properties. 

This NoSQL database type is a great option for managing diverse and complex datasets like user profiles. With a document database, you can efficiently store and access user data through its nested structures that can easily handle any changes.

Popular examples of document-oriented databases include MongoDB and CouchDB, which are both excellent open-source options. 

To get a better grasp of how document databases work, check out how MongoDB works through the video below: 

The History of NoSQL

While concepts of non-relational databases had been around since the late 1960s, the term NoSQL was not coined until the late 2000s. 

These databases were born out of necessity as data storage requirements began to soar during the early to mid-2000s to meet the needs of rapidly-growing Web 2.0 companies. 

Through the use of horizontal scaling, a simpler design, and the ability to effectively utilize machine clusters, NoSQL databases were able to overcome many of the scalability and data access problems of traditional relational databases. 

This made NoSQL databases a leading choice for managing large and complicated datasets, which are often unstructured.

The term “NoSQL” was coined by Carlo Strozzi in 1998 for his open-source relational database that didn’t use a SQL interface. This early concept is quite distinct from the modern concept of NoSQL databases, which tend to eschew a relational structure.

The contemporary concept of NoSQL databases came to the forefront in 2009, led by Johan Oskarsson—a developer at Last.fm. 

The name came about to label the rise of open-source, distributed, non-relational databases. This included Bigtable by Google and DynamoDB from Amazon, developed in 2004 and 2006. Both helped shape the modern NoSQL movement.

Is a NoSQL Database Right For You?

NoSQL databases can be an excellent alternative or companion to traditional relational databases and similar solutions. 

These databases are a huge help, especially in situations where scalability is essential such as when you need to handle large volumes of data or if you’re looking for faster query responses. 

While it’s a robust solution for many data types, it is not a perfect catch-all. Different solutions may better suit certain data sets.

Make sure to consider your particular use case carefully. Evaluate the type of data being stored and what you’re trying to do with it before completely committing to a particular NoSQL solution.