Competencies: Databases > Graph Databases
Graph Databases
Body of Knowledge
| Topic | Description | Relevance | Career Tracks |
|---|---|---|---|
Knowledge Graph Construction |
Design and implementation of knowledge graphs including node/edge modeling, relationship typing, traversal algorithms, and domain-specific ontologies for knowledge representation. |
Medium |
Knowledge Engineer, Data Architect, ML Engineer |
Neo4j |
Property graph model, Cypher query language, graph algorithms, APOC procedures |
Medium |
Data Engineer, Backend Developer, Knowledge Engineer |
RDF & SPARQL |
Semantic web standards, triples, ontologies, linked data, SPARQL queries |
Low |
Knowledge Engineer, Data Architect |
Graph Query Languages |
Cypher, Gremlin, GraphQL (for graph APIs), path expressions |
Medium |
Backend Developer, Data Engineer |
Graph Algorithms |
PageRank, shortest path, community detection, centrality measures |
Medium |
Data Scientist, ML Engineer, Backend Developer |
Graph Data Modeling |
Property graphs, hypergraphs, labeled relationships, schema design |
Medium |
Data Architect, Backend Developer |
Graph Databases at Scale |
Partitioning strategies, distributed graphs, JanusGraph, Amazon Neptune |
Medium |
Data Engineer, Infrastructure Engineer |
Graph Visualization |
Graph rendering, layout algorithms, interactive exploration tools |
Medium |
Data Scientist, Frontend Developer |
Personal Status
| Topic | Level | Evidence | Active Projects | Gaps |
|---|---|---|---|---|
Knowledge Graph Construction |
Intermediate |
association-engine — Python knowledge graph with node creation, edge relationships, traversal algorithms, coverage analysis; mathematical foundations in graph theory |
Association Engine, PRJ-domus-math: Mathematics for Infrastructure Professionals |
No Neo4j, no RDF/SPARQL, no large-scale graph processing (GraphX, Pregel) |