SQL vs. NoSQL: The Differences Explained + When to Use Each

There are two primary databases used for storing digital data: SQL (relational databases) and NoSQL (non-relational databases). Though both methods effectively store data, they differ in their structures, scalability, relationships, language, and support.

In this article, you'll learn about each type of database, how they are similar and different from one another, and how to decide which type of database is suitable for your particular data application. If you're ready to start building your data skills, consider enrolling in the IBM Data Management Professional Certificate, where you'll have the opportunity to gain experience with essential tools like SQL, databases, and spreadsheets in as little as three months. By the end, you’ll have not only gained a career certificate for your resume, but also had the opportunity to prepare to take the CompTIA Data+ certification.

What is SQL?

Structured query language (SQL) is a programming language that allows both technically and non-technically minded users to query, manipulate, and change data in a relational database.

Organized into columns and rows within a table, SQL databases use a relational model that works best with well-defined structured data, such as names and quantities, in which relations exist between different entities. Within a SQL database, tables are linked through "foreign keys" that form relations between different tables and fields, such as customers and orders or employees and departments.

SQL databases are scalable vertically, meaning that you can increase the maximum load by adding further storage components like RAM or SSD. While in some cases this may mean that SQL databases are limited by the resources available on the server, cloud-based storage and other technologies can provide more scalability with SQL. 

Learn more about SQL and relational databases in this lecture by Professor Charles Severance in the University of Michigan's Introduction to Structured Query Language (SQL) course:

What is NoSQL?

NoSQL databases are non-relational databases that store data in a manner other than the tabular relations used within SQL databases. While SQL databases are best used for structured data, NoSQL databases are suitable for structured, semi-structured, and unstructured data. As a result, NoSQL databases don't follow a rigid schema but instead have more flexible structures to accommodate their data types. Furthermore, 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 that they use multiple nodes in a cluster to handle increased workloads. This allows data architects to simply scale them by supplementing clusters with additional servers.

NoSQL non-relational databases work well with unstructured data and typically possess the following properties:

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

• NoSQL databases have a dynamic schema for unstructured data, making data integration 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 face in modern applications, especially ones that handle large volumes of rapidly changing data. These demands exist across every industry’s vertical and application domain, including IoT, user analytics, personalization, ad tech, e-commerce, gaming, and social networks.

NoSQL vs. SQL: Main differences between SQL and NoSQL

At a high level, NoSQL and SQL databases have many similarities.

In addition to supporting data storage and queries, they also 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.

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

1. Structure

SQL databases are table-based, while NoSQL databases can be document-oriented, key-value pairs, or graph structures. In a NoSQL database, a document can contain key-value pairs, which can then be ordered and nested.

2. Scalability

SQL databases scale vertically, usually on a single server, and require users to increase physical hardware to increase their storage capacities. In effect, while cloud storage options are available, SQL databases can be prohibitively expensive for businesses when dealing with vast amounts of big data.

NoSQL databases offer horizontal scalability, meaning that more servers simply need to be added to increase their data load. This means that NoSQL databases are better for modern cloud-based infrastructures, which offer distributed resources.

3. Language

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, while NoSQL databases are more flexible.

4. Support

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

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

Read more: Relational vs. Non-relational Database: The Difference Explained

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Pros and cons of SQL

SQL is the lingua franca of data. It's the language you’ll use most 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 it also has some downsides.

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 data sets, 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.

Hear more about the history and benefits of SQL in this lecture from the University of Colorado Boulder's Databases for Data Scientist Specialization:

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, modeling, 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:

• Less mature technology and difficult to manage

• Limited query capabilities

• Data inconsistency and poor performance in some complex scenarios

When to use NoSQL vs. SQL 

Deciding when to use NoSQL versus SQL is essential 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.

Here are some situations where NoSQL might make the most sense to you:

• 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 even automatically replicate data across nodes to ensure you always 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.

Is SQL for me?

If you're an aspiring data professional, you'll need to know SQL to work in the field. But what if you're someone who just works with data occasionally rather than professionally?

Knowing SQL can be a valuable skill for almost any professional who handles data—even if you're not an analyst or data scientist. If you'd like to improve your overall data literacy and potential hireability, consider taking a beginner-friendly SQL course like IBM's SQL: A Practical Introduction to Querying Databases.

Skill up with resources built for you

To continue building your expertise, explore the latest developments and in-demand skills in your industry by subscribing to our LinkedIn newsletter, Career Chat! Or, check out the following resources if you’re looking to learn more about SQL and a data scientist career:

• Take the Quiz: Which SQL Course Should You Take?

• Bookmark for later: SQL Glossary: Your Ultimate Guide to SQL Terms 

• Watch on YouTube: 7 Essential Skills Every Data Scientist Should Have 

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