Before diving deep in the comparison of SQL vs NoSQL databases, let us first know about both the types in brief.
SQL or Structured Query Language
is a programming language that is typically used to manage data in relational databases. Relational databases use tables to store data and then develop insights from that data by using common characteristics within the dataset.
A relational database uses SQL to create both data and the schema for that data, which describes fields in columns. Every record in this is called a row.
On the other side NoSQL, is a self-describing database and it does not require a schema. All the documents of NoSQL databases are JSON documents, which are complete entities that one can readily read and understand. It allows us to create different unique structures like documents, key-value pairs or graphs.
SQL vs NoSQL Databases:
Let’s discuss the differences between the two on 4 parameters –
One of the major differences between SQL and NoSQL databases is the language they use.
SQL databases use Structured Query Language for defining and manipulating data. It has more restrictions. SQL requires that you use predefined schemas to determine the structure of your data before you even begin to work with it. Your data must also follow the same structure as well, which can entail significant upfront preparation along with careful execution.
Whereas A NoSQL database features a dynamic schema for unstructured data and the data can be stored in many different ways, whether it be graph-based, document-oriented, column-oriented, or Key-Value pair. This flexibility helps to create documents without having to carefully plan and define the structure, add fields on the go, and vary the syntax from a database to another.
One of the bigger differences between SQL and NoSQL databases is in the way they can be scaled.
SQL databases are vertically scalable, which means that the load can be increased on a single server by increasing components like RAM, SSD, or CPU. Whereas, NoSQL databases are horizontally scalable, which means that they can handle increased traffic simply by adding more servers to the database.
Due to SQL being old and widely used, it has a much stronger and more developed community compared to NoSQL. There are thousands of chats and forums available where experts can share knowledge and discuss SQL best practices, continuously enhancing skills. Although NoSQL is growing rapidly, its community is still not as well defined as that of SQL.
SQL databases are based on relations that are in the form of tables. This makes them a better option for applications that require multi-row transactions. Examples can be accounting systems, banking systems, etc. NoSQL databases can have various structures such as key-value pairs, graph-based, or document-based.
[ Also Read:
Hands-On with NodeJs MySQL
Which Database to Use??
Which database is more suitable in a project depends on a number of factors. Some of these factors can be seen in the above section. Other important factors are –
The most important factor is what the data looks like.
If data is structured in nature, then a SQL
database is probably the better choice.
A SQL database is a great fit for transactions. Because each row in a SQL database can be a distinct entity (e.g. a person), and each column behaves as an attribute that describes that entity (e.g. address, job title, phone, etc.).
SQL databases are best when ACID properties are needed-
– Every transaction is either completed in full or is rolled back.
– Any Data written to the database must be valid according to all pre-defined rules.
– When transactions are run concurrently, they do not interfere with each other, and act as if they were being run one by one.
– Once a transaction is committed, it should permanently remain in the database, even in the case of a system failure.
So, If the data is very structured or ACID compliance is a must, SQL is a great choice.
On the other hand, if data requirements aren’t clear or if the data is unstructured, NoSQL may be the better choice.
As mentioned before, the data stored in a NoSQL database does not need a predefined schema. This provides much more flexibility and less upfront planning when managing any database.
NoSQL databases are best when BASE properties are needed-
Basically Available –
This means that while the database guarantees the availability of the data, the database may fail to obtain the requested data or the data may be in a changing or inconsistent state.
Soft state – The state of the database can be changing over time.
Eventual consistency –
The database will eventually become consistent, and data will propagate everywhere not in real-time but eventually.
The BASE model was built for maximum flexibility.
Ability to query data
The next factor involves considering how often the data is queried, how quickly the queries should run, and who will be responsible for running these queries.
SQL efficiently executes queries and retrieves the data quickly. It is very lightweight and declarative and is also easy to learn. Queries can even be run by less technical staff.
Whereas, A NoSQL database because of it’s large differences in data structures, querying isn’t as efficient as compared to a SQL database.
To run NoSQL queries, Some extra processing on the data is required. Also, in some cases, concepts like MapReduce may also be needed.
Querying NoSQL databases typically requires a higher skill set as compared to SQL ones.
Choosing the correct database for any organization is a very important decision.
NoSQL databases are quickly emerging and becoming a major part of the database landscape. They have several benefits like including lower cost and easier scalability, Anyone thinking of Big Data integration, needs to use NoSQL for its management.
On the other hand, SQL databases have been used traditionally with well-defined structures. They have a huge community of experts behind them, and the opportunity for collaboration is limitless.
Overall, the decision of using SQL vs NoSQL database is not entirely black and white. It requires some comparing and contrasting to determine which database best fits the needs. Some of the factors were mentioned in the blog. There can be several other factors varying from organization to organization.