NoSQL vs SQL databases: what to choose for your eCommerce project?

Learn the differences between SQL and NoSQL databases in 2024. We analyze the pros and cons of MongoDB, MySQL and PostgreSQL for your eCommerce project, with practical use cases.

SQL: structure and reliability

Pros of SQL(MySQL, PostgreSQL):

1. Rigid Structure: ideal for highly structured data such as product catalogs and inventories.
2. Relationship Support: perfect for managing relationships between data, such as orders and customers.
3. Mature Language: SQL is standardized and supported by numerous analysis and reporting tools.

Cons of SQL:

1. Less flexible for unstructured data or variable patterns.
2. Complexity in horizontal scalability.

SQL Use Case

An eCommerce that sells thousands of products with fixed characteristics (e.g., SKU, price, quantity) can benefit from the robustness of PostgreSQL to ensure consistency and security in the data.

NoSQL: flexibility and scalability

Pro of NoSQL (MongoDB)

1. Flexibility: ideal for semi-structured or unstructured data such as reviews, logs or user-generated content.
2. Horizontal scalability: perfect for handling large volumes of distributed data.
3. Speed: optimal for high-performance reads and writes.

Cons of NoSQL:

1. Lack of standardization.
2. Not ideal for handling complex data relationships.

NoSQL Use Case

An eCommerce that operates a marketplace with independent sellers and needs to integrate custom data (e.g., promotions, user configurations) can benefit from MongoDB for its flexibility.

SQL case: eCommerce B2C

A traditional online store that needs a system to manage orders, customers, and inventory with complex data relationships.

NoSQL case: applications in B2B environment

For B2B projects, MongoDB has been used to save large price lists with customized discounts per customer. This flexibility allows it to adapt to the complex pricing dynamics and negotiations typical of B2B.

In another case, during the porting of a system for Mohd, MongoDB was used to aggregate data from multiple sources, making it available for subsequent import procedures. This approach simplified data management and improved efficiency.