Graph Databases vs Relational Databases: Key Differences

This helps recommendation systems identify related products and purchasing patterns.

For example, someone buying a laptop may receive recommendations for compatible accessories based on product relationships and customer behaviour.

4. Logistics and Transportation

Logistics companies manage routes, warehouses, delivery locations, vehicles, and suppliers.

These entities form complex networks.

Graph databases can help analyse route connections, identify alternative paths, and examine supply chain dependencies.

This can support route planning and network analysis.

5. Healthcare Information Systems

Healthcare data connects patients, doctors, hospitals, treatments, medications, and medical records.

Graph databases can help authorised systems explore these relationships.

For example, a research platform may analyse connections between treatments, patient groups, and reported outcomes.

Healthcare applications must still follow strict privacy, security, and access-control requirements.

6. Knowledge Graphs and AI Applications

AI systems often need to connect information from different sources.

Knowledge graphs represent entities and their relationships in a structured way.

For example, a knowledge graph may connect a company with its products, founders, locations, suppliers, and industry.

This helps applications retrieve contextual information instead of treating every data point separately.

Graph databases can support knowledge graph storage and querying. They can also work alongside vector databases and other systems in AI architectures.

For a related topic, explore autonomous AI agents and how they work. Connected information can help some AI applications retrieve context and understand relationships between entities.

When Should You Choose a Relational Database?

A relational database is often the better option when your application mainly handles structured records and transactions.

Consider using one when:

• Your information fits clearly defined tables.
• The application handles financial or transactional operations.
• Data consistency is a major requirement.
• Queries mainly involve filtering, sorting, and aggregation.
• Your team already works with SQL.
• The application has predictable relationships and reporting needs.

For example, a payroll application needs employee records, salary details, tax information, and payment history.