• SQL: relational database
• NoSQL: non-relational database

Relational database is a viable option most times, it is unsuited for large datasets and big data analysis.

Language

SQL manipulate the data based on SQL which is one of the most versatile and widely-used language options available. While this makes it a safe choice especially for complex queries, it can also be restrictive. This is because it requires the use of predefined schemas to determine the structure of data before you work with it and changing the structure can be quite confusing

NoSQL database which has a dynamic schema for unstructured data. In here, data is stored in many ways which means it can be document-oriented, column-oriented, graph-based, etc. This flexibility means that documents can be created without having a defined structure and so each document can have its own unique structure.

Scalability

SQL databases are vertically scalable. This means that the load on a single server can be increased by increasing things like RAM, CPU or SSD. (More floors can be added to this building).

NoSQL databases are horizontally scalable. This means that more traffic can be handled by sharding, or adding more servers in your NoSQL database.

NoSQL can ultimately become larger and more powerful, making NoSQL databases the preferred choice for large or ever-changing data sets.

Property followed

SQL databases follow ACID properties (Atomicity, Consistency, Isolation and Durability). NoSQL database follows the Brewers CAP theorem (Consistency, Availability and Partition tolerance).

Schema Design

A schema refers to the blueprint of a database i.e how the data is organized.

The poor database admins couldn’t find a table in NoSQL because there is no standard schema definition for NoSQL databases. They are either key-value pairs, document-based, graph databases or wide-column stores depending on the requirements. NoSQL databases are much better suited for big data as flexibility is an important requirement which is fulfilled by their dynamic schema.

If those database admins had gone to a SQL bar, they certainly would have found tables as SQL databases have a table-based schema. This difference in schema makes relational SQL databases a better option for applications that require multi-row transactions such as an accounting system or for legacy systems that were built for a relational structure.

Community

SQL is a mature technology and there are many experienced developers who understand it. Also, great support is available for all SQL databases from their vendors. There are even a lot of independent consultants who can help with the SQL database for very large scale deployments.

On the other hand, NoSQL is comparatively new and so some NoSQL databases are reliant on community support. Also, only limited outside experts are available for setting up and deploying large scale NoSQL deployments.

Q&A

Is NoSQL faster than SQL?

In general, NoSQL is not faster than SQL just as SQL is not faster than NoSQL. For those that didn’t get that statement, it means that speed as a factor for SQL and NoSQL databases depends on the context.

SQL databases are normalized databases where the data is broken down into various logical tables to avoid data redundancy and data duplication. In this scenario, SQL databases are faster than their NoSQL counterparts for joins, queries, updates, etc.

On the other hand, NoSQL databases are specifically designed for unstructured data which can be document-oriented, column-oriented, graph-based, etc. In this case, a particular data entity is stored together and not partitioned. So performing read or write operations on a single data entity is faster for NoSQL databases as compared to SQL databases.

Is NoSQL better for Big Data Applications?

The NoSQL databases for big data were specifically developed by the top internet companies such as Google, Yahoo, Amazon, etc. as the existing relational databases were not able to cope with the increasing data processing requirements.

NoSQL databases have a dynamic schema that is much better suited for big data as flexibility is an important requirement. Also, large amounts of analytical data can be stored in NoSQL databases for predictive analysis. An example of this is data from various social media sites such as Instagram, Twitter, Facebook, etc. NoSQL databases are horizontally scalable and can ultimately become larger and more powerful if required. All of this makes NoSQL databases the preferred choice for big data applications.

Reference

https://www.geeksforgeeks.org/sql-vs-nosql-which-one-is-better-to-use/