A Practical Guide to Graph Databases
summary
Overview of graph databases with hands-on specifics: why graphs beat SQL for relationship traversals, when they don’t, and how to model for performance. Covers query languages and examples (Cypher vs Gremlin, SPARQL), hosted engines and APIs (Amazon Neptune, Cosmos DB Gremlin API), numbers (≈43 stores surveyed, 800% popularity rise since 2013) and concrete patterns—recommendation engines, fraud detection, replacing recursive CTEs—with modeling pitfalls like supernodes, schema guidance and operational tradeoffs.
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chapters (13)
- 0:00Intro
- 1:53Graph Databases are hot
- 3:49The NoSQL Spectrum
- 6:11Why use a graph database?
- 9:27Databases vs. Frameworks
- 10:37RDF/Triple Stores vs. Labeled Property Graphs
- 19:15Visualization
- 24:29Pattern Matching
- 25:07Clustering, Centrality, and Influence
- 28:38Northwind Data Models
- 34:15Datastore Concerns
- 39:22Data Modelling Concerns
- 49:30Advice from the trenches...
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