Intro to Databases Comparing Relational and Non-Relational Databases

Learning Objective: This lesson will clarify the fundamental differences between relational and non-relational databases, helping students decide which to use based on specific application requirements.

Exploring the differences and use cases of different databases

While both relational and non-relational databases serve the purpose of persisting data, they do so using different methods.

Schema vs. schema-less databases

Icons representing relational and non-relational databases

The core distinction between relational and non-relational databases lies in their approach to structuring data:

Schema in relational databases:

A relational database uses a predefined schema to organize data into tables.
Each table consists of rows and columns, where columns define the data type and structure.
This rigid schema ensures consistent data entry and supports complex queries for data.

Schema-less in non-relational databases:

Non-relational databases, like MongoDB, do not require a predefined schema.
They allow dynamic data storage, supporting various data formats without needing a fixed structure.
This flexibility is ideal for handling unstructured or semi-structured data and can quickly adapt to changes in application requirements.

Selecting the right database for your application

The choice between a relational or non-relational database largely depends on an application’s specific needs:

Relational databases for transaction-heavy applications:

Relational databases are ideal for financial applications (for example, banking or stock trading) where handling database transactions with accuracy and consistency is paramount.
These databases struggle with non-uniform data that cannot be neatly organized into structured tables.

Non-relational databases for unstructured data:

These databases are suited for applications dealing with large amounts of diverse data, like social media platforms.
MongoDB, a popular document-based non-relational database, offers high flexibility. It is a great choice for rapidly changing and evolving data models, as often seen in prototyping stages.