Editing Graph Database (section)

=== Database Options ===
{| class="wikitable"
! Database
! Key Features
! Pros
! Cons
|-
| '''Neo4j'''
| Optimised for relationship-heavy queries and traversals. Uses Cypher query language, which is relatively simple.
| 
* Excellent for multi-hop traversals.
* Strong visualisation tools.
* Relationship properties natively supported.
| 
* Limitations on community edition but for contract tracing etc its probably enough
* Costs for enterprise edition.
* Requires setup and hosting.
|-
| '''AWS Neptune'''
| Fully managed service supporting Gremlin and SPARQL.
| 
* AWS integration.
* Scalable and highly available.
| 
* Higher cost.
* Steeper learning curve for SPARQL/Gremlin.
|-
| '''TigerGraph'''
| Distributed graph database for large-scale analytics.
| 
* Excellent performance for deep relationships.
* Handles large datasets well.
| 
* Enterprise-focused pricing.
* Requires learning GSQL.
|-
| '''PostgreSQL + Apache AGE'''
| Extends PostgreSQL to support graph functionality.
| 
* Cost-effective.
* Integrates with existing relational workflows.
| 
* Limited advanced graph features.
* Not natively supported on RDS
* Requires self-hosting PostgreSQL.
|-
| '''ArangoDB'''
| Combines graph, document, and key-value models.
| 
* Flexible for mixed workloads.
* Cost-effective.
| 
* Less specialised for graph-specific tasks.
|}