Graph Databases vs Relational Databases: Key Differences

Its data model uses three main elements:

Nodes: These represent entities such as people, products, businesses, locations, or accounts.

Relationships: These represent connections between nodes. Examples include PURCHASED, WORKS_FOR, OWNS, and LOCATED_IN.

Properties: These store additional details about nodes and relationships, such as names, dates, prices, and status.

Consider an online shopping platform.

A customer buys a smartphone. That smartphone belongs to a particular brand and category. Other customers may purchase the same model or related accessories.

A graph database can represent these connections directly.

This makes it useful when an application needs to understand how different entities are connected.

Popular graph database technologies include:

• Neo4j
• Amazon Neptune
• TigerGraph
• ArangoDB
• JanusGraph

Graph databases are commonly used for recommendation systems, fraud detection, social networks, knowledge graphs, and network analysis.

How Do Relational Databases Work?

Relational databases organise information into structured tables. Applications retrieve related records using queries.

Consider a simple ecommerce example.

A business maintains three tables:

Customers

Orders

Products

When a customer places an order, the database stores the relevant customer and order information.

If the application needs to find Rahul's orders, it uses the Customer ID to connect the relevant records.

SQL JOIN operations help retrieve information from multiple tables.