Graph Databases vs Relational Databases: Key Differences

Developers and analysts can explore how customers, products, transactions, and organisations connect.

This is useful for identifying patterns that may be difficult to spot through isolated records.

4. Useful for Fraud Detection

Fraud often involves connections between multiple entities.

Several accounts may share the same device, address, phone number, or payment method.

A graph database can help investigators explore these connections and identify suspicious patterns.

5. Supports Recommendation Systems

Recommendation engines need to understand relationships between users, products, preferences, and previous activities.

Graph databases can help identify connections between customers with similar interests.

This supports product recommendations, content discovery, and personalised experiences.

Real-World Applications of Graph Databases

Graph databases are especially useful in industries where understanding relationships can improve decision-making.

1. Social Networking Platforms

Social platforms contain millions of relationships between users, groups, pages, and content.

A graph database can represent these connections and help identify mutual friends, communities, and user interests.

For example, a platform may recommend a person based on shared connections and common group memberships.

2. Banking and Fraud Detection

Financial institutions need to identify suspicious activity across accounts and transactions.

A single transaction may appear normal. However, connections between several accounts, devices, and payment methods may reveal a larger pattern.

Graph databases help analysts explore these relationships.

They can also support investigations involving organised fraud networks and suspicious transaction chains.

3. Ecommerce Recommendations

Online stores need to understand how customers interact with products.

A graph database can connect customers, purchases, product categories, brands, and reviews.