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A Brief Introduction to Graph Databases

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Read the book online: "https://graphtechnologydevelopers.github.io/brief-intro-graph-databases/"

About This Book

Graphs model the interconnected fabric of real-world data—from social ties to supply chains, biological networks to fraud patterns. Traditional databases like RDBMS handle structured rows well, but as relationships proliferate, queries bog down with expensive joins. Graph databases prioritize these connections, delivering fast traversals even in massive, evolving datasets.

This manual expands on core concepts for developers, blending theory with practical how-tos. Expect deeper dives into modeling, querying, and implementation, with code samples in Cypher and Python, plus Mermaid diagrams for clarity. 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.

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

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License

This book is available as open source under the MIT License.

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