NoSQL vs SQL Databases: Understand the Differences and When to Use

Written by Coursera Staff • Updated on

Learn from this comparison of NoSQL with SQL performance, an explanation of the NoSQL and SQL database structures, and the considerations you can make to choose NoSQL or SQL.

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You’ll discover two primary technologies when storing digital data: SQL (relational databases) and NoSQL (non-relational databases).  These options differ in structure, scalability, relationships, language, and available support.

Understanding the differences between NoSQL and SQL databases will allow you to decide better which database solution is suitable for your particular data application.

What is NoSQL?

NoSQL doesn’t mean a lack of relations. Nor does it mean that you’re limited to only one relation per table. NoSQL describes that you can have data in a semi-structured document column. This document may have a schema and be completely different from others in the same column. 

Although NoSQL databases have existed since the 1960s, they only achieved widespread popularity recently with the rise of Big Data and IoT applications. Instead of using SQL to query the database, NoSQL databases use varying query languages (some don't even have a query language).

NoSQL databases are scalable horizontally, meaning they use multiple cluster nodes to handle increased workloads. This allows you to scale them by supplementing clusters with additional servers.

NoSQL databases work well with unstructured data. NoSQL databases have the following properties:

  • NoSQL is schema-less (no fixed data model).

  • NoSQL databases have a dynamic schema for unstructured data, making integrating data in certain types of applications easier and faster.

  • NoSQL uses non-tabular data models, which can be document-oriented, key-value, or graph-based. The most common NoSQL databases include MongoDB, Cassandra, HBase, Redis, Neo4j, and CouchDB.

NoSQL manages the scale and agility challenges you may have in modern applications, 

especially ones that handle large volumes of rapidly changing data. These demands exist across every industry vertical and application domain, including IoT, user analytics, personalisation, ad tech, eCommerce, gaming, and social networks.

What is SQL?

Structured Query Language (SQL) is a programming language. It allows technical and non-technically-minded users to query, manipulate, and change data in a relational database. SQL databases are scalable vertically. You can increase the maximum load by increasing components like RAM or SSD. In many cases, this limits SQL by the resources available on the server, although cloud and other technologies can provide more scalability with SQL. 

SQL databases work well with structured data where relations exist between different entities. They are table-based, meaning they structure data into rows and columns within a table. These tables are linked through foreign keys to form relations between tables and fields, such as customers and orders or employees and departments.

Main differences between NoSQL and SQL

Here are some of the main differences between SQL and NoSQL databases:


SQL databases are table-based, while NoSQL databases can be document-oriented, key-value pairs, or graph structures. A document contains key-value pairs that are ordered and can be nested.


SQL databases scale vertically, usually on a single server. To scale a SQL database, you have to increase hardware. This can make applications prohibitively expensive when dealing with vast amounts of data. NoSQL databases are horizontally scalable. To scale a NoSQL database, you add more servers. This is better for modern cloud-based infrastructures, which offer distributed resources.


SQL databases use SQL (Structured Query Language). NoSQL databases use JSON (JavaScript Object Notation), XML, YAML, or binary schema, facilitating unstructured data. SQL has a fixed-defined schema.


SQL is a popular standard language well supported by many different database systems, while NoSQL has varying levels of support in other database systems.

Regarding support, you’ll generally find that more help is available for SQL databases than 

NoSQL. This is because SQL is a more established technology and thus has many more users and developers who can help you with your problems. In contrast, NoSQL is still relatively new, with less help available on forums or through the community. Your support options may be limited if you run into difficulties using it.

At a high level, NoSQL and SQL databases have many similarities. They both support data storage and queries and allow one to retrieve, update, and delete stored data. However, under the surface lie some significant differences that affect NoSQL versus SQL performance, scalability, and flexibility.

Pros and cons of NoSQL

A significant benefit of NoSQL is that you don't have to define a schema upfront (or ever). This makes it easy to add new columns without dealing with all the issues involved in altering a vast table with lots of data already in it. It also means that if your queries don't require SQL, you can avoid the overhead of parsing and compiling SQL statements, modelling, and storing, providing an enormous performance boost when dealing with large amounts of data.

However, NoSQL is less mature than SQL. Here’s a look at NoSQL's pros and cons.

Pros of NoSQL:

  • Flexible schema

  • Usable on distributed infrastructure platforms

  • Low-cost infrastructure

  • High availability and throughput

Cons of NoSQL:

  • NewerLess mature technology and difficult to manage

  • Limited query capabilities

  • Data inconsistency and poor performance in some complex scenarios

Pros and cons of SQL

SQL is the universal language of data. It's the language you’ll use to query databases and move structured data between traditional applications. It's a powerful language that can help you do many data-related things, but also has some issues. Here are some pros and cons of using SQL for data storage and retrieval.

Pros of SQL:

  • SQL is widely understood and supported; most developers know it well.

  • SQL is extremely useful for simple aggregations over large datasets, such as calculating averages.

  • SQL is extremely useful for setting up simple ETL jobs, especially if the input and output formats are relational databases.

  • SQL is well-documented and easy to learn.

Cons of SQL:

  • The performance of SQL can be poor on substantial data sets because it requires multiple passes over the data to complete many operations (especially joins). 

  • Debugging SQL can be complicated because it doesn't provide informative error messages.

  • The syntax of SQL tends to be verbose compared with programming languages like Python or R, which makes it harder to write complex transformations as scripts or functions.

How do I know when to use NoSQL or SQL? 

It is essential to decide when to use NoSQL versus SQL because they differ in structure, capabilities, and ideal use cases.

A relational database like SQL is a great option if you’re looking to build an application structured around a relationship between data tables. SQL also works well when you want to ensure your data is consistent across tables. However, relational databases aren’t always the best choice regarding flexibility or scaling.

A non-relational NoSQL database doesn’t use structured tables but instead uses flexible schemas for unstructured data storage. This gives you more ability to scale your project as needed. However, it also means you have less control over consistency and data relationships.

Is SQL for me?

It can be fun to learn something new, and SQL can introduce you to the world of data management. With the guided project Introduction to Relational Database and SQL, you’ll gain hands-on experience working with a relational database. At the end of 60 minutes, you should be able to create a billing report and a club member roster.


Here are some situations where NoSQL makes sense:

  • You need high performance, particularly read performance: The way distributed NoSQL systems like Cassandra and Riak work means you can usually get very high read performance by adding more boxes. Some go so far as to automatically replicate data across nodes to ensure you have plenty of copies of your data to access.

  • You need high availability (HA): Data replicates across nodes in a NoSQL system, so the failure of a single node does not necessarily result in data loss or downtime for your application. This also means you can easily add or remove nodes from clusters without impacting availability.

Next steps 

SQL and NoSQL are two different approaches to storing and manipulating data. While SQL databases have been the traditional choice for app developers, NoSQL databases have become increasingly popular over the past few years. If you're new to databases, having a firm grasp of SQL and NoSQL is a good idea. Each has its use case.

To start your learning, consider the Introduction to NoSQL Databases course offered by IBM and the Introduction to Structured Query Language (SQL) course provided by the University of Michigan.

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