If you’ve spent any time researching database architectures, you may have encountered the terms “SQL” and “NoSQL” and wondered what’s the difference between these two tools.
It’s a great question, and one that many people new to databases often have. Although both contain the word SQL, there are many differences between them.
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To help you understand how SQL and NoSQL differ, we are going to do a side-by-side comparison of the two architectures, addressing their similarities and differences so that you are in a better position to decide which one to use.
We begin by discussing the basics of SQL and NoSQL. We then compare them using four key points. By the end of this article, you’ll be able to make an informed decision about which architecture you should use in your project.
What is SQL?
Structured Query Language (SQL), pronounced “S-Q-L” or “See-quel,” is a query language that allows you to work with relational databases.
Relational databases use relations (also known as tables) to store data. These tables can be connected together to create more complex storage facilities. The SQL language can be used to easily and efficiently insert records, update, search for records, and delete records in a database.
Some of the most common relational database systems that use the SQL architecture are Oracle, Access, PostgreSQL, and Microsoft SQL Server.
What is NoSQL?
The term NoSQL is self-explanatory enough—it is a non-relational database management system. Unlike SQL-based databases, the NoSQL architecture does not use a fixed schema.
NoSQL supports a dynamic schema for unstructured data. Rather than in tables, data is stored using the unique structure you define.
The NoSQL database structure is typically used for databases that need to store massive amounts of data. Google, Netflix, and LinkedIn, companies that have large data set needs, use the NoSQL structure in their applications.
The SQL structure uses a standardized syntax to retrieve and store data, whereas NoSQL uses a wide range of different technologies that allow you to manipulate the data in a data store.
What’s the Difference Between SQL and NoSQL?
So far, we’ve given you some background on these technologies. Now, let’s tackle the big question: how are SQL and NoSQL different?
We can look at four main aspects to compare these technologies:
- Language: This refers to the syntax that governs each technology.
- Scalability: How each system is known to operate as it grows to store more data.
- Structure: The architectural structure adopted by each database management technology.
- Learning Curve: How easy—or difficult—it is to learn.
Let’s explore each in more detail.
One of the most important differences between SQL and NoSQL is the language each uses.
SQL-powered databases use Structured Query Language to store and manipulate data. SQL has a wide range of commands and rules that you can use to effectively work with data stored in a database. However, in order to work with SQL, your database needs to have a defined schema, which can take a while to create.
NoSQL, on the other hand, is not dependent on a specific schema; the language is said to have a dynamic schema. This means the data stored in a NoSQL database can use columns, documents, a key-value store structure, or another structure entirely.
This is advantageous because it allows you to store data without creating an elaborate structure first, and makes it easier to update the structure of your database.
Both SQL and NoSQL are scalable, but the extent to which each architecture is scalable differs in some ways.
SQL databases are vertically scalable. This means you can increase the capacity of a server by boosting its CPU power or other computational metrics.
NoSQL databases are horizontally scalable. To handle more queries, you must add more servers to the database, or practice a technique called “sharding” to inject new computational power into the database.
An SQL server can scale on the same machine, but NoSQL servers typically scale by adding more servers into a network.
SQL is still a highly scalable structure, but if you are working with massive datasets, the scalability of NoSQL can be extremely beneficial.
SQL and NoSQL both use different structures.
SQL-based databases use tables, which means data is stored in columns and rows. Each column reflects a value that can be stored in a table, and each row represents an entry into the table.
This structure works in a wide range of applications. For instance, if you want to build a blog, a table-based structure is great; if you are building an e-commerce site, a table structure should do it.
NoSQL systems, on the other hand, can store key-value pairs, documents, graph databases, and more, which means the architecture is inherently more flexible.
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Before investing time to learn a new technology, it’s a good idea to spend some time considering the learning curve—how easy it is to master the technology. Consider whether you have the time to master the technology.
Between SQL and NoSQL, there is a clear winner: SQL. SQL was invented in the early 1970s, and since then has become an essential part of data storage.
The SQL language is taught in a large portion of data science and data analysis courses and tutorials precisely because it is so widely used. Almost every company that needs to store data, from small businesses to giants like Amazon, use SQL to some degree. As a result, there is a wide range of resources out there to help you master SQL.
In addition, because SQL is standard, once you learn a set of underlying rules, you can build anything using the architecture.
NoSQL is still well-documented, and enjoys a wide community of supporters. However, despite the quick growth in demand for NoSQL skills, the architecture is still new compared to SQL, so there are fewer support networks to help you learn it.
SQL vs. NoSQL: Which One to Use
Selecting the right database architecture for a project—or for you to learn—is a big decision, and one that you should not take lightly.
Learning SQL is a great investment because of its ubiquity; you’ll have no trouble finding a company that can use your SQL skills. Although its table-based structure may be restrictive at scale, for most applications, SQL works just fine. It can, in fact, be the better-performing option for smaller use cases.
However, if you intend to work with larger data structures that do not have any clear schemas, learning NoSQL could be worth your time. In terms of flexibility and scalability, NoSQL is the winner. There is no doubt that, as more companies recognize the importance of big data, NoSQL will grow in popularity.
So, which one should you learn or use? Well, it’s up to you. SQL has proven to be a reliable technology, but if you need something that can scale quickly and effectively, and does not use a rigid data structure, NoSQL may be worth a look.
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