Relationships are central to recommendations, identity, and network analysis. The Practitioner's Guide to Graph Data explains how to model those connections before selecting a storage engine, giving graph-shaped questions a clear starting point.
From Relationships to Queries
Nodes, edges, traversal, paths, graph technologies, and queries are introduced alongside applications such as fraud detection, entity resolution, knowledge graphs, and Customer 360. Ingestion, consistent identities, query performance, partitioning, and keeping derived relationships current connect the model with production work.
The distinction between modeling and infrastructure is useful. Expressing a problem as a graph can clarify it even when a relational database remains sufficient for implementation.
Choosing the Right Context
Developers and data engineers working with connections, neighborhoods, or paths will find a practical perspective. A graph database makes sense when relationships are central to the queries and the benefit justifies operating another system. Sketching the model first helps assess that need.
Get the Book
- O'Reilly: The Practitioner's Guide to Graph Data