Feature image Why NoSQL databases are more scalable_

Why are NoSQL Databases More Scalable?

By Brian Laleye · November 11, 2022 · 7 min read

It becomes a tedious job if you go for choosing the best architecture. The scalability factor becomes crucial for designing an application such that any data stored in this is scalable efficiently and quickly.

Nowadays, choosing one of the databases from the multiple available options becomes quite challenging. The biggest question here is why NoSQL is more scalable. We will discuss it in this article.

SQL databases have been in use for a long time back. It is a well tested, mature and reliable database. It has been a long time since these databases have served us well.

More flooding of data

Data increases exponentially daily as the number of operations gets multiplied to generate more complexity. Time passed when the data of 1TB/GB was significantly large. Now we are dealing with large-size data. Thus we need the best data stores that can be easily scalable.

Why is SQL database not scalable?

SQL database stores are excellent for processing data of size TB in an efficient way. However, more big-size data creates problems. This is basically due to the inherent data structures and the architecture design that are entirely based on the use cases of SQL databases.

For any SQL operation, the engine fetches all the needed data in memory for performing SQL operations such as aggregation, joining, filtering, etc. If you talk about bigger data sizes, it means more data in memory and more CPU cycles. Moreover, when the data goes beyond the limit of TB, it becomes difficult for single machines to handle such data efficiently.

Indexes are the first line of defense to optimize operations. Even after some applied indexes, B-Tree requires loading and manipulating data in memory for the search and update. The data memory in a device gets its limit due to the natural limitation of the CPU. Moreover, if more than one query runs parallel, the performance will downgrade automatically as it is eating the CPU power.

The next challenge faced is ‘Join’ operations. It is one of the most powerful features of SQL. This data is kept in a normalized form which can be later fetched by joining data from multiple tables. It is one of the most powerful features. However, it is one of the slowest also. Join requires fetching data from various tables in memory and then combining these data with the help of join logic. The more the data is, the slower it would respond.

How does NoSQL overcome these challenges?

NoSQL is designed in such a way to handle extensive data with the feature of scalability. One of the crucial architectural assumptions is that data can be spread in different machines. As the data is kept in multiple machines, handling large data operations on low-powered devices becomes tedious.

NoSQL Databases perform sharding in every operation by default. The database always expects a qualifier for doing data operations that identifies the data stored node.

Therefore any particular data operation hits a specific node only. Usually, the data is spread across multiple nodes, where each set has a particular form of data on which you can perform operations. Therefore it becomes fast and efficient. If doing control over the data on the machine, the simple devices also can be employed to add more CPU and memory in our overall ecosystem.

How does NoSQL solve the slowness challenge due to 'Joins.'

NoSQL considers that the user working will make a better plan to structure the data so that all the necessary required data is easily fetchable in one flow from the same node. It is also called demoralizing the data across various nodes.

Moreover, various frameworks and concepts can already pre-cook and process the data effectively. If at any stage, there arises a need to merge the data from more than one source, there is the option for the application layer to manage. In most such cases, the data is pre-planned and pre-cooked.

Benefits of the Approach:

  • Data is easily distributable across multiple machines. Therefore these kinds of simple machines also enhance your CPU power efficiently. There is no need for big machines. In this, they don’t have a vertical scaling limit. Thus, NoSQL becomes highly scalable.
  • It processes the data operation faster as the machine can perform parallel data operations.

Database Scaling

SQL databases are vertically scalable. However, you can enhance the load on a single server with the help of adding RAM, CPU, or SSD.

Moreover, NoSQL databases are horizontally scalable. The higher traffic can be handled by sharding that adds more servers to your NoSQL databases. Regarding the comparison between vertical and horizontal scaling, horizontal scaling possesses a greater overall capacity than vertical scaling. As a result, NoSQL becomes the preferable choice for large changing sets of data.


There are many differences between NoSQL and SQL, which are equally crucial for making the right decision in choosing the best data management system. Some of the essential points are:

  •   Scalability
  •   Structure
  •   Community
  •   Language

The most significant difference between them comes with the feature of scalability. More importantly, SQL databases are more vertically scalable, which means they can be scaled or increase the load on a single server by adding components such as RAM, SSD, CPU, etc.

Moreover, NoSQL databases are horizontally scalable. It means that significant traffic can be handled easily with the help of the addition of more servers to databases. NoSQL databases are best as they can become more powerful and more prominent. These features make it a preferred choice for large sets of data.


In this article, we have learned about SQL and NoSQL. Afterward, we discussed why the NoSQL database is not scalable. Nextly, why NoSQL is scalable and how it works. We compared at last between two databases to reach a final result to say why NoSQL is suitable for the scalability of an application.


At RestApp, we’re building a Data Activation Platform for modern data teams with our large built-in library of connectors to databases, including MySQL, data warehouses and business apps.

We have designed our next-gen data modeling editor to be intuitive and easy to use.

If you’re interested in starting with connecting all your favorite tools, check out the RestApp website or try it for free with a sample dataset.

Discover the next-gen end-to-end data pipeline platform with our built-in No Code SQL, Python and NoSQL functions. Data modeling has never been easier and safer thanks to the No Code revolution, so you can simply create your data pipelines with drag-and-drop functions and stop wasting your time by coding what can now be done in minutes! 

Play Video about Analytics Engineers - Data Pipeline Feature - #1

Discover Data modeling without code with our 14-day free trial!



Subscribe to our newsletter

Brian Laleye
Brian Laleye
Brian is the co-founder of RestApp. He is a technology evangelist and passionate about innovation. He has an extensive experience focusing on modern data stack.

Related articles

Build better data pipelines

With RestApp, be your team’s data hero by activating insights from raw data sources.

Activate and combine any data sources without code

Transform your data with our No Code SQL, Python and NoSQL functions

Run automatically your data pipelines and sync modeled data with your favorite tools

Share your pipelines and collaborate smarter with your teammates

Discover how Data Transformation really means

Find out the new data architecture concept of Data Mesh

Learn how Operational Analytics actives your data

Learn how to deliver great customer experience with real-time data


Crunch data at scale with ease

Configure connectors, no build

Save time & efforts for data prep

Save time & efforts for data prep


Stay always up to date on data activation

Get access to tips and tricks to model your data

Discover our always evolving and regularly updated documentation

Find out how we keep your data safe