SQL and NoSQL represent two popular types of database technologies and every developer should know why they are important.
And when it comes to managing databases, one of the biggest decisions organizations, developers, data scientists, and sometimes even project managers have to make is whether to use SQL or NoSQL.
So what are SQL and NoSQL? What is the difference between these two? How can you tell which will work best for what projects?
This is exactly what you will find out in this article. The key difference between these two database technologies is the way they store data. SQL is a relational database system meaning you’ll need to define how you will store data ahead of time. While NoSQL is a non-relational meaning doesn’t require you first define how data will be stored.
What is SQL (and what is a relational database)?
SQL is an acronym for Structured Query Language. It’s pronounced as “S-Q-L” or “See-Quel”.
Today, SQL is by far the most popular database language (see below trend chart from Google Trends), and one of the most in demand tech skills for business analysts, data scientists, and any profession that deals with data.
It’s the standard programming language used to query and manipulate data in what are called “relational” databases.
Relational database management was introduced way back in the 1970s by E.F. Codd, while he was working as a researcher for IBM.
How does SQL work?
Think of SQL like an Excel file. Excel has a sheet or grid of columns and rows. That sheet is similar to what you find in an SQL database only it is called a “table”. And Excel can have multiple sheets – you guessed it – SQL databases can have multiple of these sheets or “tables”.
Think of a “relational” database where all those sheets or tables can be connected to each other using relationships or “keys”. Meaning there is a column in one table (table A) that is designed to only point to a column in another table (table B). And every time new data or a new “row” is added to table A then it must point to an existing row in table B.
And then it is possible to ask for or “query” data from two or more tables when there is a relationship setup between tables. Hence the term “relational” database.
By asking for or querying data from a relational database we mean you might be using a database program like Microsoft’s SQL Server Studio (popular for MS SQL) or PHPMyAdmin (popular for MySQL). Or you could use the command line or other software applications might be connecting and retrieving or adding data.
What is NoSQL (and what is a non-relational database)?
NoSQL is a non-relational database technology or what some call a distributed database system.
It stands for “Not Only SQL”, “Not SQL” or “None SQL”.
Non-relational databases don’t have a fixed schema. They deal with unstructured data that you can’t easily or don’t want to fit into tables.
NoSQL was developed with a focus on flexibility, ease of scaling, and to hold lots and lots of what are called unstructured data (or “documents”).
Although the NoSQL movement started way back in the early 90s, its popularity only took off in the mid 2000s.
And the development of non-relational database systems by Amazon, Microsoft and Google (Amazon’s Dynamo, Microsoft’s Cosmos DB and Google’s BigTable) certainly helped popularize NoSQL databases – because they scale very easily and those providers stand to make lots of money doing that 😉
Let’s take a NoSQL example:
- If you build a relational database it means you have to think about and setup your database tables ahead of time. Not the case with non-relational (NoSQL) – you could just start inserting data and it doesn’t really matter what format that data is in.
- If you want to make changes to your database after data is inserted, you’d need to add, remove or be careful to adjust columns in your tables. This isn’t a bad thing, but adding new columns means each existing row now has a blank value in that column and represents potentially waisted space. This kind of rigidity led to the development of NoSQL as an alternative database system.
And there are four main types of NoSQL database systems (document, key-value, wide-column, and graph).
How does NoSQL work?
We’ll take a simple example using the document type. In this example, a document in NoSQL is like a very simple array or JSON string. where the “Name” and “Role” are the keys and the “Rey” and “Jedi” are the values. So our document has two key value pairs.
You can think of each document in a NoSQL database like a row in a SQL table. Except each document does not need to be exactly looking like all other documents.
I could for example insert the following two documents into the same NoSQL database. Notice how Document 2 has an extra key value pair, telling us Ben’s grandfather is in fact Darth Vader, but you knew that already from Star Wars Episode VII 😉
So adding extra key/values is OK, it does not need to be defined within the setup of the database ahead of time and is why NoSQL is popular to store unstructured data.
Then documents are organized into what are called a “Collection”. There can be multiple collections in the database. And a Collection in NoSQL is like a table in SQL.
In the end, a NoSQL database let’s you store data that is unstructured meaning the data does not need to always have the same format (each document can be different). And this gives a lot of flexibility over time as the format for new data may change however it does not mean the setup or architecture of the database needs to change.
SQL vs. NoSQL: The Differences
The differences between SQL and NoSQL go beyond relational vs non-relational.
Specifically, the two technologies differ in language, data structure, scaling, and even the size of the community.
Let’s explore each of these differences in more detail below.
SQL databases use Structured Query Language to define and manipulate data. SQL is quite versatile as a language, but the structured nature makes it very restrictive.
The data in a SQL database has to follow the same structure for all data inserted and the developer has to define a schema beforehand. As a developer, this means that you have to take extra care when designing and building your SQL database.
On the contrary, NoSQL gives you extreme flexibility regarding how to execute your database. This is because it uses a dynamic schema. You can get started creating your database right away without having to define the structure in the beginning.
With NoSQL you will be able to add or remove fields within your documents on the fly (when you insert them), use a unique structure for each document or vary the syntax between documents.
SQL databases have vertical scalability. What this means is that you scale up by migrating to a larger server, which can be quite costly. It’s possible to scale SQL databases horizontally across machines, but it gets complicated.
On the other hand, NoSQL databases allow data to easily scale horizontally. Because of the way data is stored using a sort of lookup approach. And means you can grow NoSQL databases by adding commodity servers where each server takes on a portion of the overall database, which can be cheaper and more effective than scaling vertically.
When it comes to structure, SQL databases are table based. Data is organized into rows and columns.
With NoSQL, you are not tied to a tabular data format. You can store data as a graph, in columns, in document format, or organize it as key value pairs.
Since SQL has been around longer, it only makes sense that it has a much more robust community of developers.
Many forums exist where SQL professionals come together to share knowledge and best practices.
But even though NoSQL is relatively a newcomer, it’s popularity is growing in leaps and bounds. And its community is following suit.
SQL vs NoSQL Advantages and Disadvantages
Advantages of SQL Databases
- Friendly Syntax: An advantage to developers, especially those just starting out, is that SQL syntax is easy to read and understand.
- Referential Integrity: SQL enforces the relationship between tables. This ensures transactions are complete, and free of orphaned (or bad) records. SQL is also ACID (Atomicity, Consistency, Isolation, Durability) compliant also strengthens data integrity.
- Resource Efficiency: Developed in the 1970s when disk memory and space were at a premium, SQL was focused on reducing data duplication. SQL databases are smaller and thus lead to efficient use of resources at hand and faster performance.
- Stable Language: SQL has been around for decades. It’s a mature language with well-defined standards and plenty of expert users.
Disadvantages of SQL
- Requires vertical scaling, which doesn’t come cheap. Horizontal scaling is very challenging with SQL.
- It requires developers to use a predefined structure and set schema. This demands careful up-front design, which means more work for developers.
- Queries in SQL databases can become slow as they may require joining data from multiple tables.
Advantages of NoSQL Databases
- High Scalability: NoSQL supports seamless horizontal scalability. The databases can meet high availability requirements as applications and websites grow and don’t have single points of failure.
- Flexibility: Developers don’t have to commit to a specific structure right from the start. They can add features to their data on the fly.
- High Performance at scale: Since many NoSQL databases are distributed and scale horizontally they can achieve extremely high levels of performance as data grows relative to a large vertically scaled SQL database.
- Fast Queries: NoSQL databases are optimized for fast queries. The queries don’t require joining data from different tables so data can be retrieved pretty fast. This is because when data is inserted data can be pre-joined together.
Disadvantages of NoSQL Databases
- Most NoSQL databases don’t support ACID transactions across multiple documents. An exception here is MongoDB, which offers support for multi-document ACID transactions.
- With the exception of general purpose databases, many NoSQL databases are only optimized for specific use cases.
- NoSQL is a relatively young and immature technology. It’s not as well-developed and stable as SQL.
So Which Database System To Use?
Both SQL and NoSQL are actually great database technologies, and both are widely used in the modern world.
However, it’s clear that these two database languages are on opposite ends of the spectrum.
Each language has strengths and weaknesses that make it suitable for certain types of databases as follows.
When To Use SQL?
Being a relational database, SQL is a no brainer when you need to define relationships between data ahead of time (for example a table for bank accounts, a table for transactions, etc).
And when data integrity is top priority, the tried and true SQL based relational database is also a sure bet.
Or if you intended to build an application that will store lots of the same type of data or you have different data that needs to be dynamically joined together as needed (from different tables), SQL may be a good choice.
SQL is the technology used in accounting systems and transaction databases, as it requires consistency and has no room for error.
The top relational database management systems (RDBMS) that use SQL include:
- Oracle (Many Fortune 500 companies use Oracle for transactional business applications.)
- MySQL (The most popular open source SQL) or MariaDB (a fork of MySQL)
- Microsoft SQL Server
- Microsoft Access
When To Use NoSQL?
Whenever you’re dealing with messy or unstructured data you can’t fit into tables, NoSQL comes to the rescue.
NoSQL databases are ideal for managing unstructured, semi-structured, and polymorphic data (that’s where data formats that could change at any time).
Thanks to it’s horizontal scalability, NoSQL databases are best suited for storing and analyzing large quantities of data and still having really good database performance.
NoSQL database systems are widely used in big data operations because of their affordability and ease of scaling. Twitter, Facebook, and Google use it to manage a staggering amount of data on a daily basis.
Even though relatively new, NoSQL has plenty of open source projects. There is a database category for pretty much any use case so you can choose the technology best optimized for the type of data scenario:
- General purpose document databases like MongoDB or CouchDB.
- Key-value databases like Redis and Amazon’s DynamoDB for fast access and retrieval of large amounts of data:
- Wide-column databases such as Cassandra and HBase organize data based on predictable query patterns.
- Graph databases like Neo4j and Amazon Neptune for analyzing relationships in data.
- Elasticsearch allows flexible or complex search over a lot of data.
What About “NewSQL” and “Distributed SQL”?
As you’ve seen, both SQL and MySQL have strong advantages and also some disadvantages.
Opting for one language over the other means having to make some tradeoffs.
To offer the best of both worlds, databases that combine aspects of relational and distributed databases are also being developed.
- NewSQL systems add distributed logic to relational databases. Citus and Vitess are examples of NewSQL database engines.
- Distributed SQL systems are more aspirational than NewSQl databases. They aim to build horizontally scalable relational databases. CockroachDB and Google’s Spanner are examples of distributed SQL engines.
Where to Learn SQL
Here are two great resources to get you up to speed with SQL:
The Complete SQL Bootcamp 2021: Go from Zero to Hero
The Complete SQL Bootcamp 2021 is designed to take you from zero to SQL pro.
Learn how to use SQL and PostgreSQL to query databases and analyze data.
Gain skills to use other top SQL databases as well : MySQL, Microsoft SQL Server, Amazon Redshift, Oracle, and more.
A digestible structure, wonderful examples, and hands on exercises make the course understandable even for beginners.
SQL QuickStart Guide: The Simplified Beginner’s Guide to Managing, Analyzing, and Manipulating Data With SQL
Hands down, SQL Quickstart Guide is the best SQL book for beginners.
It covers everything you need to know to use SQL. You will gain an understanding of relational databases and the ability to apply SQL at work.
Whether you’re looking to expand your programming skills or enhance your career, this book is great. Best of all, no prior coding experience is required.
Where to Learn NoSQL
Here are some great resources to get started you with NoSQL databases:
NoSQL Databases For Beginners
NoSQL Databases For Beginners is a great course to learn about NoSQL databases.
Learn how to store, retrieve, and query data in NoSQL databases. Understand the different categories of NoSQL databases and how to create and secure non-relational databases.
NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence
NoSQL Distilled does an excellent job of explaining NoSQL to someone with zero prior experience of distributed database system. It offers a quick way to get up to speed for a project.
You will gain an understanding of how NoSQL databases work and how to evaluate whether NoSQL is right for your projects and the specific NoSQL technologies you should consider.
Finally, you will learn how to implement NoSQL. Realistic use cases and examples simplify learning.
To Wrap Up
Both the present world and the future are data driven. Now more than ever there is a need to handle massive databases quickly and efficiently.
SQL and NoSQL are currently the most popular tools of data management.
SQL databases have been around for more than four decades and have well defined standards and a robust community of users. SQL is very widely used and a highly reliable database technology.
NoSQL is currently the shiny new thing in the data world. It’s gaining widespread use because of its flexibility and ease of use.
Those who advocate for NoSQL databases argue that they can be used to build high performance systems that scale better when you don’t know what your data will look like ahead of time. Those liking SQL argue it’s better at storing and accessing data when you know what your data will look like.
Understanding the differences between SQL and NoSQL databases is key to making the right decision on which database system to use – they are both great in their own ways 🙂
Mike is the creator of Go With Code and a coder at heart 🙂