Welcome to the complete guide to graph databases. This book covers everything from fundamental concepts to practical implementation, with code samples in Cypher and Python, plus Mermaid diagrams for clarity.

Table of Contents

1. Welcome to Connected Data

2. Why Graphs Matter

3. Graph Theory Primer

4. Core Concepts: Nodes and Edges

5. Graph Models Compared

6. Data Organization Techniques

7. Querying Graphs: Languages That Click

8. Data Modeling Basics

9. Data Modeling Pitfalls to Avoid

10. Why Ditch Relational for Graphs?

11. NoSQL Context: Where Graphs Fit

12. Native vs Non-Native Processing

13. Graph Algorithms Essentials

14. Use Cases: Graphs in Action

15. Popular Graph Databases

16. Distribution and Transactions

17. Future Challenges

18. Imperative vs Declarative Queries

19. Pitfalls in Graph Modeling

20. Getting Started with Neo4j

21. Wrapping Up

22. Acknowledgements

About This Book

This manual expands on core concepts for developers, blending theory with practical how-tos. Expect deeper dives into modeling, querying, and implementation. It’s distilled from key sources but written briskly: why graphs outperform alternatives, how to build and query them, and tips for real apps.

Data explodes, but value lies in links. Spot fraud rings via cycles; recommend via paths. Ignore graphs, and your systems miss hidden insights.