Significant differences between SQL and NoSQL
- NoSQL databases do not have a strict schema, whereas SQL databases have.
- Relationships exist in SQL databases, however they do not exist in NoSQL databases.
- Tables make up SQL databases, whereas collections and documents make up NoSQL databases.
- SQL databases have several tables, but NoSql databases contain only a few collections.
- NoSQL databases are both vertically and horizontally scalable, whereas SQL databases are just vertically scalable.
- SQL databases may have limits on the number of queries per second, however NoSQL databases do not.
Let’s look at what SQL and NoSQL databases are and how they work now that we’ve seen the fundamental differences between them. We’ll also examine and contrast the benefits and drawbacks of both databases.
Structured Query Language (SQL)
SQL stands for Structured Query Language, which is a database query language. At the end of the day, SQL is just a programming language, not a database. However, SQL can be used to query databases. A standard SQL query would look like this:
SELECT id, name, email FROM users;
The capitalized keywords in this sample query are called SQL keywords, while the other portions of the query are custom columns that are required and the name of the database table.
Of course, SQL has many more keywords and instructions. SQL can be used to conduct any database CRUD activities (Create, Read, Update, and Delete).
When we talk about SQL vs. NoSQL, we’re referring to the databases that run them.
A relational database is the most common database for SQL. It means we have a database that operates under specific assumptions and supports structured query language.
Tables are used in such a database. Consider a table to be a container for a certain type of data. A users table and a products table, for example. The information about our application’s users is stored in the users table, and the information about the products is stored in the products table.
Tables in relational databases are linked together by common columns known as keys. In a relational database, there are primary keys and foreign keys. Primary keys are used to uniquely identify records in a table, whereas foreign keys are used to link records in other tables.
However, there are some tight limitations on how we can store data in relational databases, where we query using SQL. This is because the database must adhere to a predetermined schema. Each column has a unique data type that cannot be altered. Even adding a new column to the table does not provide you the flexibility you desire. Let’s imagine you need to save some more data, such as a customer’s special order instructions. You should ideally have the particular instructions information for that specific order. However, this is not achievable with SQL.Even if this implies all other records will have NULL values, you’d have to add a new column called special instructions to the entire table. While this isn’t a horrible idea, it’s also not very adaptable.
However, SQL is a powerful tool. Despite the difficulties it introduces owing to the various joins that must be completed each time a request is sent, SQL remains the preferred choice for many developers. SQL will be incredibly useful if you want to go into data science or are already doing it. SQL is a fantastic data wrangling tool that can accomplish pretty much anything that Python can.
Let’s look at what NoSQL is and how it works now that we’ve covered SQL.
Not Only SQL, but also NoSQL
NoSQL stands for “Not Only SQL” since, by its own nature, it permits the storage of unstructured data. Unlike SQL databases, NoSQL databases do not have a defined schema.
In fact, unlike SQL databases, NoSQL databases lack tables and indexes. So, how do we save information? We store data in a hierarchical format in NoSQL. Information is kept in collections and documents rather than tables. Consider NoSQL database collections to be like SQL database tables, and NoSQL documents to be like SQL records.
Unlike SQL tables, which have a set schema, NoSQL collections have no such restriction. The papers can include as many values as necessary. It is now possible to have distinct information in each document because to this extraordinary amount of flexibility. Returning to the SQL section’s special instruction column example, it is now able to add the special instruction column only for that specific order without affecting the entire collection.
Even though there is no such thing as a “relational” database in NoSQL, it is still possible to gather all important information in one place. That is, by putting all of the data you require into a single collection. Yup. This may appear inefficient, but it dramatically decreases query complexity and allows us to display data rapidly without the need to combine numerous tables like we would in a SQL database.
Scaling horizontally with SQL databases is difficult/impossible. With NoSQL databases, however, this is not the case. But what is horizontal/vertical scaling, anyway?
When you add more servers to your database as your data grows, this is known as horizontal scaling. Vertical scaling, on the other hand, simply adds extra processing power to an existing server. One major disadvantage of SQL databases is that scaling them further becomes increasingly difficult. However, with NoSQL, you can simply add more servers as needed to scale and enjoy a stress-free existence!
Both have advantages and disadvantages.
- A set schema is helpful for a predictable layout and dependable database fields, but it’s not very flexible. SQL has a predictive layout but lacks flexibility, whereas NoSQL has both flexibility and field dependability.
- SQL databases can readily manage update operations because of the relationship between tables. Due to the lack of a set schema, NoSQL is better at handling read requests but not update operations.
- Scalability may be a challenge when developing a long-term application. Because of its capacity to scale horizontally, NoSQL is a viable option.
Practically, either of these databases can be used to create any type of application. It all comes down to your specific requirements and the kind of your company. Only when the data becomes large does a problem occur.
Do you require a large number of data relationships that you will alter frequently? Do you require a well-defined schema? Choose SQL.
Do you have a lot of read requests but few write requests in your application? Want to see data fast without having to run complicated queries? Is scaling a problem? Choose NoSQL.
There is no clear victor between these two, and it all depends on the application’s specific requirements.