Graph Databases
A graph database stores data as entities and their explicit connections. It suits questions that follow many relationships, such as how people, services, accounts, or products relate.
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Intro
Graph Databases
A graph database stores a domain as nodes and relationships. A node represents an entity. A relationship records how two entities connect. Both can carry properties. The model makes the connections first-class data, so you can ask questions by following them.
The central design move is to start with questions, not tables. Write the questions your application must answer. Identify the entities and the verbs that connect them. Then model the minimum nodes, relationship types, and properties needed to answer those questions. Neo4j's modeling guide uses exactly this sequence: domain, use cases, data model, sample data, and testing.
What graph databases are for
Graph databases fit data where the relationships are part of the question. Examples include access relationships, dependency maps, fraud rings, recommendations, network topology, and knowledge graphs. In each case, you care about a path or neighborhood, not only one record.
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Sources
- https://neo4j.com/docs/getting-started/appendix/graphdb-concepts/
Supports
- A property graph has nodes, typed directed relationships, labels, and properties
- Relationships connect source and target nodes and can have properties
- A traversal visits nodes by following relationships and a path records the visited sequence
- Indexes support performance and constraints enforce domain rules
- https://neo4j.com/docs/getting-started/data-modeling/tutorial-data-modeling/
Supports
- Data modeling begins by defining the domain and application use cases
- A graph data model describes nodes, relationships, labels, types, and properties
- Sample data and queries test whether a model satisfies its use cases
- Specific relationship types and unique identifiers improve a usable model
- https://tinkerpop.apache.org/docs/current/reference/
Supports
- Apache TinkerPop documents graph computing, graph structure, traversals, and Gremlin
- The reference includes traversal steps, graph features, transactions, and provider integrations
- https://opencypher.org/resources/
Supports
- openCypher publishes resources for the Cypher query-language ecosystem
- https://graphacademy.neo4j.com/
Supports
- Neo4j GraphAcademy provides official courses for graph fundamentals, Cypher, data modeling, importing data, and graph data science