Graph Databases Guide

A graph database stores entities as nodes and relationships as first-class edges, so queries that traverse many connections, such as fraud rings, recommendations, supply chains and knowledge graphs, avoid the repeated joins a relational database needs. Two models dominate: labeled property graphs, where nodes and edges carry properties, and RDF, where everything is a subject-predicate-object triple.

Property graph databases suit application development, traversal-heavy analytics and developer-friendly query languages. RDF triple stores suit data integration, global identifiers, standards-based reasoning and validation (SPARQL, SHACL, OWL), and publishing linked data. Many teams use one model and bridge to the other with tooling; some engines (such as Amazon Neptune) support both.

Each model has its own languages, and a new ISO standard is bringing property graph queries together.

Native graph databases built around the property graph model.

Graph alongside documents, key-value or SQL.

Managed graph offerings from the major clouds.

Evaluate and experiment.

Match the model to the job: choose RDF when you need standards, data integration and reasoning; choose a property graph for application traversals and developer ergonomics; consider multi-model or relational extensions if you already run PostgreSQL or a document database. Test candidates on your own queries and data size, check the licence and the query language, and consider GraphRAG needs such as vector search alongside the graph.