When developing a new software project, the most important thing is choosing the right tools, and one of the most important tools is the database engine.

Below we will explore the pros and cons of SQL vs. NoSQL database engines, helping you make an informed decision which is best for your project. Although akin to the PC vs. Mac debate, this article will strive to be as objective and non-biased as possible.

SQL (mySQL, PostgreSQL, Oracle, etc.)

Without getting into the differences between specific engines, relational SQL databases are still the most widely used database engines throughout the world. Developed throughout the 1970s, SQL was first released as a language in 1979, and still today remains the dominant language for communication with relational databases.

Since SQL is the de facto industry standard, developers who are well versed with it can easily transition between working with different database engines.

Relational databases require a pre-defined schema which consists of tables and columns, with each record being a row within a table. Although schemas can be easily modified at any time, this does require some pre-planning to ensure all necessary data fits into the database properly. Columns can be one of any multitude of various data types including strings, integers, floats, large text elements, binary blobs, and so on.

Relational Databases

The structured design of relational databases allows you to easily create child-parent relationships between tables.

For example, the "id" column within the "users" table is linked to the "userid" of the "notes" table. With support for cascading, when a parent row is deleted or updated, all child rows will be affected as well. This helps to not only always ensure structural integrity, but also allows for optimal performance and speed when performing queries against multiple tables.

However, properly architecting and managing a large database schema can be a task in and of itself, and one many developers have opted out of. With large databases, modifying the schema can also be time consuming and require proper preparation.

On the flip side, the structured design can lend itself to an easier pathway for other developers who work with the software, as they can plainly see how the database is structured.

NoSQL (MongoDB, etc.)

With MongoDB leading the pack by a healthy margin, NoSQL databases have gained huge popularity over the last good handful of years. This is mainly attributed due to its schemaless structure meaning no pre-defined database schema, and its use of JSON objects for records providing familiarity to developers.

Instead of tables and rows, NoSQL databases use collections and documents. There is no requirement to pre-define the database schema, and instead everything is automatically created on the fly. For example, if you try to insert a document into a non-existent collection, instead of throwing an error, the collection will be automatically created on the fly.

Documents are JSON objects, which provide great familiarity since JSON is already used on a daily basis by developers. Since documents have no defined structure, any and all data may be stored within them, and may differ between documents.

This provides great flexibility as not only is time saved from not creating and managing a database schema, but you may add arbitrary data into any individual document without an error being thrown due to database constraints.

Less Structural Integrity

Although NoSQL does provide great flexibility and familiarity, the one downfall is its lack of support for constraints causing less structural integrity than its SQL counterparts. With no solid support for relationships between collections or cascading it can lead to issues such as orphaned child records being left behind in the database after their parent record has been deleted, and reduced optimization for handling related records across multiple data sets.

The structureless design can also lead to additional undetected bugs within the software. For example, if a developer makes a typo and puts "amont" into the code instead of "amount", a NoSQL database will accept it without throwing an error or warning.

SQL vs. NoSQL: Which Database Is Best?

As usual when it comes to software development, the answer is, it depends.

For example, if you have a need to store more unstructured data such as insurance, educational financial or genealogy records then NoSQL would make a great choice as its schemaless structure allows you to insert additional arbitrary data into documents.

However, if you have a need for larger records that span multiple tables with priority being placed on structural integrity and query performance, then SQL is probably a better choice.