Most databases can be categorized as either relational or non-relational. Non-relational databases are sometimes referred to as “NoSQL,” which stands for Not Only SQL. The main difference between these is how they store their information.
A non-relational database stores data in a non-tabular form, and tends to be more flexible than the traditional, SQL-based, relational database structures. It does not follow the relational model provided by traditional relational database management systems.
To explain non-relational databases in more detail, let’s first look at what a traditional, relational database is.
A relational database typically stores information in tables containing specific pieces and types of data. For example, a shop could store details of their customers’ names and addresses in one table and details of their orders in another. This form of data storage is often called structured data.
Example customer relational database table.
Relational databases use Structured Query Language (SQL). In relational database design, the database usually contains tables consisting of columns and rows. When new data is added, new records are inserted into existing tables or new tables are added. Relationships can then be made between two or more tables.
Relational databases work best when the data they contain doesn’t change very often, and when accuracy is crucial. Relational databases are, for instance, often found in financial applications.
Non-relational databases (often called NoSQL databases) are different from traditional relational databases in that they store their data in a non-tabular form. Instead, non-relational databases might be based on data structures like documents. A document can be highly detailed while containing a range of different types of information in different formats. This ability to digest and organize various types of information side by side makes non-relational databases much more flexible than relational databases.
Example MongoDB Document for a Patient in Healthcare.
Non-relational databases are often used when large quantities of complex and diverse data need to be organized. For example, a large store might have a database in which each customer has their own document containing all of their information, from name and address to order history and credit card information. Despite their differing formats, each of these pieces of information can be stored in the same document.
Non-relational databases often perform faster because a query doesn’t have to view several tables in order to deliver an answer, as relational datasets often do. Non-relational databases are therefore ideal for storing data that may be changed frequently or for applications that handle many different kinds of data. They can support rapidly developing applications requiring a dynamic database able to change quickly and to accommodate large amounts of complex, unstructured data.
When starting a project, it is worth considering relational vs. non-relational databases, in terms of their differences, to get a better understanding of the right solution for the project. You can also consider different examples of the uses for both, and when you might want to choose one over the other.
Today’s applications collect and store increasingly vast quantities of ever more complex customer and user data. The benefits of this data to businesses, of course, lie in their potential for analysis. Using a non-relational database can unlock patterns and value even within masses of variegated data.
There are several advantages to using non-relational databases, including:
Massive dataset organization
In the age of Big Data, non-relational databases can not only store massive quantities of information, but they can also query these datasets with ease. Scale and speed are crucial advantages of non-relational databases.
Flexible database expansion
Data is not static. As more information is collected, a non-relational database can absorb these new data points, enriching the existing database with new levels of granular value even if they don’t fit the data types of previously existing information.
Multiple data structures
The data now collected from users takes on myriad forms, from numbers and strings, to photo and video content, to message histories. A database needs the ability to store these various information formats, understand relationships between them, and perform detailed queries. No matter what format your information is in, non-relational databases can collate different information types together in the same document.
Built for the cloud
A non-relational database can be massive. And as they can, in some cases, grow exponentially, they need a hosting environment that can grow and expand with them. The cloud’s inherent scalability makes it an ideal home for non-relational databases.
Applications must be able to query data efficiently and deliver results almost instantly. Non-relational databases are a natural choice for this kind of environment. They offer both security and agility, allowing for rapid development of applications in an agile environment. Easier and less complex to manage than relational databases, they can also yield lower data management costs while providing superior performance and speed.
A natural fit for agile development, non-relational databases can accommodate the complexity of data inputs more efficiently than structured databases. In an age of increasing data complexity, non-relational databases provide the flexibility in database design that has become increasingly indispensable. Especially when paired with the cloud, non-relational databases lift the limits on your data collection, organization, and analysis, allowing you to get the most out of your data.
NoSQL (Not Only SQL) is any kind of database system that does not follow the tables-and-rows structure that forms relational databases.
They are more flexible, scalable, and are often developed with the cloud in mind. To learn more, there is an article that discusses the advantages of NoSQL, or non-relational databases.