Graph Databases

In a property graph, nodes and relationships carry labels, types and properties. Other graph systems use RDF statements and different query languages. Queries can find a pattern, follow paths or aggregate connected records. This differs from a knowledge graph, which adds domain semantics and often provenance to the represented relationships. It also differs from GraphRAG, an application architecture using graph-organized evidence for generation. A graph database may support indexes or vector features, but its defining role is storing and querying connections under its own transaction, query and operational model.

The practitioner selects a data model based on actual relationship queries and establishes identifiers, constraints and update rules. It tests traversal depth, cycle handling and query cardinality on representative graph shapes. Ingestion must preserve relationship direction and source identity. Useful artifacts include the schema, query library, performance measurements and recovery procedures. When a language model proposes queries, application code restricts operations and validates the query before execution. Read and write permissions are enforced through database and application controls rather than graph terminology alone.

Connected storage can make bad relationships easier to propagate. High-degree nodes or unbounded paths can also create expensive queries, and a graph model may complicate simple tabular reporting. Product capabilities vary, so selection should follow workload evidence rather than a claim that connected data always requires a graph database. Correctness depends on identity, relation semantics and update quality in addition to the database's ability to execute a traversal.