Data tips

SQL vs NoSQL: What's the Difference and When to Use?

Learn the difference between SQL and NoSQL and when to use one over the other.


SQL and NoSQL are two popular types of databases with key differences, primarily, SQL is relational, whereas NoSQL is not. Over the course of this blog post, we will discuss what SQL and NoSQL are and when to use them. Let's jump right into it.

What is SQL?

SQL (Structured Query Language) is a language that can be used to interact with relational databases. SQL is the standard language that is used to perform CRUD (create, read, update, delete) operations across all Relational Database Management Systems (RDBMS). People love SQL because it provides a rigid structure which makes working with data in your database quite simple. SQL has been around since the late 1970s and is industry standard across RDBSs, so there are lots of resources available for you to learn and problem-solve.


SQL databases store data in tables. Each table has columns that represent different attributes of your data, and rows which are called entries or records. SQL databases follow a rigid structure and columns have type constraints to enforce consistency.

Every table has a column that stores the primary keys, which are unique identifiers for each row. Primary keys are extremely important as they are used to link different tables in your relational database. For example, an e-commerce shop's database would have a table for orders and a table for customers. In the customer table, the primary key would be the customer ID, and that can be stored as an attribute in the order table as well. By doing this, you can easily associate a customer with their orders.

Enforcing a rigid structure has pros and cons. The main benefit of consistency is that data becomes easier to work with after it is in your database. All data fits the designated schema, so querying it is simple.

On the other hand, getting data into your database can become more difficult, as you need to mold it to fit your schema. However, with the help of modern tools like Dropbase, the ingestion process is becoming easier than ever.


SQL databases scale vertically, which means that you can handle more traffic by upgrading your server's hardware (e.g. RAM, CPU). While vertical scaling is a viable option, the power of your server is limited by the current state of technology.

Sharding can also be used to handle additional traffic, however, sharing SQL databases can get quite complex. Another option is to have multiple copies of your database, some of which will handle read traffic and others will handle writes.

What is NoSQL?

Unlike SQL databases, a NoSQL database does not have to follow a rigid structure. NoSQL is said to have gotten its name from "not only SQL". You don't need to have a set schema or even tables; rather, there are many different implementations of NoSQL databases depending on your application.

NoSQL databases are non-relational and can handle structured, unstructured, or semi-structured data. NoSQL databases can use many different data structures like key-value pairs, graphs, documents, or columns to store your data, which can make it better suited for specific applications.

In the last ten years, NoSQL has gained a lot more popularity.


There are many types of NoSQL databases, but today will will focus on the four most common:

  1. Document databases: Data is stored in documents similar to JSON. Documents have fields and values that can be of many different types. Document databases are relatively easy to query and due to the dynamic schema of NoSQL databases, they can adapt over time. Implementations of document databases differ, however, one of the most common implementations is to group documents into collections. In this model, the collection is similar to a table in a SQL database, while the documents are similar to the records.
  2. Key-value databases: Each item in the database has a key and an associated value. The key is used to find the value very quickly. Key-value databases are popular when working with large amounts of data and you know what you are looking for.
  3. Wide-column databases: These databases use tables, rows, and columns to store data, however, unlike SQL databases, the columns may differ for each row in the same table. These databases are more similar to a two dimensional key-value store.
  4. Graph databases: Data is stored in nodes and edges. Nodes often store information about people, places, things, etc. while edges are the relationships between nodes. Graph databases are very popular for social networks.

Although SQL is known for handling relational data, NoSQL databases can also deal with related data, it is just done differently. NoSQL stores related data within a single data structure. Although this uses more storage, as there will be duplicated data (e.g. storing school addresses stored with every student), querying this data will not require combining data from multiple tables.

One of the main benefits of NoSQL databases is the flexibility that comes with them. For all of the different architectures discussed, data can change over time without causing conflicts. This is highly beneficial when you don't know how your data will change over time.


NoSQL databases are horizontally scalable, which means you can handle more traffic by adding more servers to your database. Horizontal scaling is often preferable to vertical scaling, as there is no limit to the number of servers you can have, whereas there is a limit to the power of a single server. Even as your database gets very large, you can maintain the same high performance.

When to use SQL?

There are many reasons for using SQL, here are some of the situations where using SQL would be preferable to NoSQL.

  1. Complex queries and reporting: If you are working with complex queries and want your data presented in a specific and consistent way, SQL is likely the better option.
  2. No anticipated changes to data: If your data will always follow the same structure, using a NoSQL database might be overkill. SQL databases can handle structured, consistent data very well.
  3. Relational data: Both NoSQL and SQL databases can handle relational data, however, SQL can do so while occupying less memory and using organized tables. It might require more effort to create a schema for your data, but querying with it will be easier.
  4. Consistency is a must: If you are building a database for a payroll system or a bank, consistency is extremely important. In situations like these, the rigid structure of a SQL database is useful.

When to use NoSQL?

Here are some of the situations where NoSQL would be preferable.

  1. Data is constantly changing: It is difficult to predict how your application, and as a result your data, will change over time. Using NoSQL allows for different fields and data types without having to restructure your database.
  2. Unstructured, unrelated data: This is where NoSQL excels. There are far fewer limitations on what you can store and how you need to store it. You can have a mix of structured and unstructured data.
  3. Want to get set up fast: Using NoSQL allows you to have a dynamic schema so you don't need to spend time thoroughly planning how you are going to store your data. This is great when following practices like Agile or Scrum where you need to build prototypes quickly.
  4. Significant scale needed: Scaling a NoSQL database is extremely easy because you can scale horizontally. If you are anticipating having huge amounts of data that needs to be accessed in near real-time, NoSQL might be better for you.


SQL and NoSQL both have their advantages and disadvantages, and you should pick which to use based on your personal needs. There are even situations where using both SQL and NoSQL might make sense. The main differences come down to structure. If you have unstructured or inconsistent data, NoSQL is probably for you.

Insights and updates from the Dropbase team.
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