When it comes to selecting what format of databases, practitioners are often faced with a choice between constructing using SQL or NoSQL. While both of these have their places in data management, they become evermore different as time goes on, and each becomes increasingly versatile.

In this article, we’ll be taking a look at the central differences between SQL and NoSQL, pointing them out and explaining why one may favor a certain type of data management system. Let’s get right into it.

Exploring the five central differences between SQL and NoSQL

Choosing between a non-relational (NoSQL) and relational (SQL) will push data managers to take a look at the core differences between these two databases. Although both have their own set of pros and cons, there are a few core differences that mark these as distinct ways of managing data.

Typically, the five differences between SQL and NoSQL that are cited are:

• Process
• Language
• Structure
• Scalability
• Ideal Uses

Let’s break these down further.

Process

One central difference between these two database systems is that SQL uses ‘ACID’ when resolving transactions, and NoSQL uses ‘CAP’.

ACID is an acronym that represents atomicity, consistency, isolation, and durability, representing the four process rules that must be exhibited when a transaction is run. Breaking these down further, you have:

• Atomicity – Nothing within SQL can ever be partially completed. This is an atomic system where everything succeeds or fails; there is absolutely no middle ground.
• Consistency – Every step within this system will follow the rules; it is a regulated system and must therefore always move through invariants as it runs. This ensures that nothing is corrupted and that there is always consistency in the results pool. Even when running an SQL debug, the results will be consistent.
• Isolation – Even if two processes were run at the same time, they would always be kept separate from each other. This isolation ensures that the final state is always consistent, as it cannot be impacted by anything else that may happen to be running at the same time (this is similar to C).
• Durability – Once a transaction is executed, it will continue until the end. As SQL cannot stop midway, the impact of executing something is final on this system.

When it comes to running transactions on NoSQL, the system adheres to ‘CAP’, which stands for Consistency, availability, and Partition Tolerance. Let’s break these down:

• Consistency – Errors can occur in NoSQL, which differs from SQL’s atomic system. What is consistent about this system is that a request will either receive the most recent result or an error, there are no other options.
• Availability – Even if there is an error, every request that is executed through this system also provides a non-error result.
• Partition Tolerance – Partition tolerance is the concept that even if a cluster breaks between two nodes (a lack of direct communication or pause in it), the cluster as a whole will still continue to work and run.

The distinct processes followed are one of the most fundamental differences between these two database systems, which is why we’ve addressed them first in this list.

Language

As the world of business continues to turn to alternative data sets, wider pools, and flexible forms of data collection, NoSQL has rapidly developed. As this is a database format that allows for a range of different data structures in one place, this serves modern environments that have to deal with flexible datasets. Due to this, the languages behind NoSQL don’t typically have the interface that SQL offers, nor any support for complex queries.

On the other hand, SQL is a legacy system, dating back to 1979 and having over 42 years under its belt. Partly due to its relational structure and partly due to the sheer amount of time that it’s been around, SQL is widely documented and very recognizable to the majority of developers.

Specific dialects of SQL can vary, but this doesn’t change any of the syntax or grammar of the language used. This almost identical system allows anyone that is proficient in one form of SQL to quickly translate their skills into other formats.

Structure

As previously stated, SQL databases are relational, meaning that they rely on certain rules and data integrity and consistency when it comes to collecting, housing, and processing data. As you can imagine, for a very structured system, SQL databases are made up of rows and columns, with logical relationships underpinning any interrelations.

On the other hand, NoSQL databases can range in structure and style, which is mainly reflective of their broader use cases. Here are just three examples of the potential structure of NoSQL databases:

• Graphs – A graph database allows for the relationship between two objects to be explored, allowing for rapid maneuvering of lots of connected data at once. This is great for data scientists that need to work with interlinked datasets.
• Documents – In this context, documents are any objects which hold all of their own information, meaning that they are a compartmentalized unit of data. One document can be completely different from another, meaning that a document NoSQL database needs to be able to hold a variety of data structures.
• Column-orientated – Databases of relational database management systems allow lots of individual data to be stored in one place, without having to transform it to conform to structural rules that SQL would require.

As you can see, SQL has a very straightforward definition, while NoSQL can be many things, take many forms, and serve a range of functions.

Scalability

Both SQL and NoSQL databases are scalable, allowing businesses to increase the size of their database to better accommodate for the data that it needs to collect and process. However, these two formats are scalable in different directions. Let’s break this down:

• SQL – SQL databases are typically vertically scaled. What this means is you could continually increase the load on your server, whether that be in terms of CPU, RAM, or SSD usage. This scaling is about increasing the power of the database, but not necessarily what it can do.
• NoSQL – Alternatively, NoSQL can be scaled horizontally. Whether it be through sharding or continuously adding more servers that connect to your NoSQL database, this allows the whole database to become larger, more complicated, and ultimately more powerful.

That said, these systems can actually both scale vertically and horizontally. However, SQL is much better at scaling vertically, and NoSQL is more efficient when scaling horizontally. Part of what makes data engineers select one over the other is the end efficiency. While they could scale either system, if one will take significantly more time, effort, and resources, then it’s better to plan ahead and use the correct database for its intended use at the very beginning of the construction process.

Ideal Use

The type of system that you will rely on heavily depends on what you need the database for. For example, SQL databases are great for any transactions that are comprised of multiple rows. On the other hand, NoSQL databases are significantly more efficient when it comes to data that’s unstructured.

For example, for a plethora of unsorted documents or JSON files, then a NoSQL database would be much more fitting. Yet, SQL databases are typically much more commonly used as legacy systems rely on relational structures to function. With the expansion of the number of different data formats that the average data scientist will use, the use of NoSQL databases is continuously growing.

In fact, many argue that NoSQL databases are now the preferred database set when comparing these two methods due to the chaos of modern data and the constant need to process disparate sources.

Final Thoughts

At the end of the day, although both SQL and NoSQL are database systems, they have a lot less in common than you may think, considering the names. Understanding how these are different will give users the tools to correctly select the system that will benefit them best for that particular project.

Equally, understanding that there are benefits to both systems is vital – with both SQL and NoSQL serving impactful purposes within business.

Source: Plato Data Intelligence: PlatoData.io