NoSQL & SQL Databases: The Key Differences

Last time here on the blog, we focused on cloud databases along with details on traditional vs. Database-as-a-service (DBaaS), the two main environment models. Cloud databases are used extensively due to their benefits, such as scalability and low hardware costs. Adoption among organizations worldwide is rapidly accelerating because of these advantages and the trend is causing impressive market growth projections. From 2022 to 2032, the cloud database and DBaaS market are estimated to have a compound annual growth rate (CAGR) of 14%.

In terms of earnings, Microsoft overtook Amazon Web Services (AWS) by the end of 2021. Respectively, they accounted for 24% and 23.9% of the total DBMS revenue worldwide, which was roughly USD 80 billion. Oracle, Google, and IBM make up the rest of the top five with 20.6%, 6.5%, and 5.6%. Big names among cloud-native database providers like MongoDB and Snowflake also reached the top 15.

Growth forecasts and market competition like the above put a stronger emphasis on the crucial role cloud databases play in the field of development. From simple tasks like duplicating files to precision processes like load testing, they are a pillar of today’s digital infrastructure.

To understand how to maximize the capabilities of cloud databases, it’s imperative to learn about 

NoSQL vs. SQL databases and their defining characteristics. These are the two types of cloud databases, just like their traditional counterparts. In this post, we will look at their key differences.

History

The first key difference between the two types is their history and the main purpose of their development. SQL databases have been in use since the 1970s and they were created to handle data with less duplication.

NoSQL is a relatively young technology as it only got mainstream attention in 2009, albeit the concept was developed around a decade earlier. The initial purpose was to have databases that can deploy and integrate applications quickly. DevOps and Agile trends at the time pushed for this necessity.

Nature of data and storage model

Another distinction between NoSQL and SQL databases is their schema. NoSQL databases have a flexible schema, which means information or objects can be added without following pre-defined logic.

Because of this versatility, NoSQL databases come in various types. Each has specific methods for handling data. The four main kinds are:

• Document – stores documents in JSON format
• Key-value – uses key-value pairs
• Wide column – uses dynamic columns
• Graph – uses nodes and edges

It’s different in the case of SQL databases, which have a set schema. They typically adhere to the use of rows and columns for data manipulation. Following a rigid schema also requires the data to be validated. ID fields should not be empty or null and unique, which means one record in the database will be unable to reference a non-existing record.

Syntax

Most NoSQL databases have their own syntax for performing operations. For instance, MongoDB has MongoDB Shell – a command-line tool where users input the desired transaction. It has its own set of commands, created especially to be understood by the database platform.

On the other hand, SQL databases use SQL syntax, which follows a certain standard. This holds true even if there are multiple instances of SQL databases. Even if each instance is designated to perform a distinct operation, the commands used across all instances belong to the same set of rules.

ACID Properties

Atomicity, Consistency, Isolation, and Durability (ACID) properties are typically supported by SQL databases. This ensures reliability when executing operations. Meanwhile, the majority of NoSQL databases don’t support ACID transactions. It’s a trade-off in favor of performance and accessibility.

Scaling

SQL databases are commonly scaled vertically. You have to set up and run hardware and software with higher capacities so the databases can accommodate more load.

NoSQL databases may be scaled horizontally. You can deploy additional hardware and software that are not necessarily much higher than your original setup. Increasing the capacity can be achieved by using bigger nodes in the network.

Main Applications

All the qualities above denote that SQL databases are great for general purposes. They can be your typical office records like employee files or similar details. An example would be a social media platform like Instagram managing SQL databases to maintain the profile details of its users.

NoSQL databases are suited for dynamic data since they do not follow a pre-defined schema. Dynamic data may be anything from sensor time logs to media files. Using the same example above, Instagram can use NoSQL databases for managing images and videos submitted and accessed from its servers.

Conclusion

The distinct differences between NoSQL and SQL indicate that there is no “one size, fits all” approach when using cloud databases. Many companies today, especially large corporations, employ both types to get the best of both worlds. There is no wrong option as it will ultimately depend on your project or business requirements.