Unraveling the Web of Relationships: Understanding Graph Data
summary
Claims that graph databases outperform relational systems for highly interconnected data and pattern matching, with concrete examples: social graphs, recommendation hops, GPS routing and fraud-detection patterns. Covers SQL Server workarounds (adjacency lists, path‑enumeration/hierarchyid, nested sets, and SQL Graph’s partial support), and contrasts property graph versus RDF triple stores. Recommends proof‑of‑concept and budget for tooling or talent, and lists practical stacks like Neo4j with Cypher, TigerGraph and Gremlin as real options.
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chapters (13)
- 0:00Introduction
- 1:33Why graph data matters
- 2:36Graph theory history
- 3:39What is graph data
- 5:02Graph data applications
- 6:28Is your data graph-like
- 9:16Tools and budget for graphs
- 10:18Questions before graph adoption
- 11:41Graph algorithms in SQL Server
- 13:07What is a graph database
- 14:11Types of graph databases
- 15:56Graph query languages
- 16:38Relational vs graph differences
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