Graph Databases in Action

introduces you to graph database concepts by comparing them with relational database constructs. You'll learn just enough theory to get started, then progress to hands-on development. Discover use cases involving social networking, recommendation engines, and personalization.

Summary

Relationships in data often look far more like a web than an orderly set of rows and columns. Graph databases shine when it comes to revealing valuable insights within complex, interconnected data such as demographics, financial records, or computer networks. In

Graph Databases in Action

, experts Dave Bechberger and Josh Perryman illuminate the design and implementation of graph databases in real-world applications. You'll learn how to choose the right database solutions for your tasks, and how to use your new knowledge to build agile, flexible, and high-performing graph-powered applications!

About the technology

Isolated data is a thing of the past! Now, data is connected, and graph databases—like Amazon Neptune, Microsoft Cosmos DB, and Neo4j—are the essential tools of this new reality. Graph databases represent relationships naturally, speeding the discovery of insights and driving business value.

About the book

Graph Databases in Action

introduces you to graph database concepts by comparing them with relational database constructs. You'll learn just enough theory to get started, then progress to hands-on development. Discover use cases involving social networking, recommendation engines, and personalization.

What's inside

Graph databases vs. relational databases

Systematic graph data modeling

Querying and navigating a graph

Graph patterns

Pitfalls and antipatterns

About the reader

For software developers. No experience with graph databases required.

About the author

Dave Bechberger

and

Josh Perryman

have decades of experience building complex data-driven systems and have worked with graph databases since 2014.

Table of Contents

PART 1 - GETTING STARTED WITH GRAPH DATABASES

1 Introduction to graphs

2 Graph data modeling

3 Running basic and recursive traversals

4 Pathfinding traversals and mutating graphs

5 Formatting results

6 Developing an application

PART 2 - BUILDING ON GRAPH DATABASES

7 Advanced data modeling techniques

8 Building traversals using known walks

9 Working with subgraphs

PART 3 - MOVING BEYOND THE BASICS

10 Performance, pitfalls, and anti-patterns

11 What's next: Graph analytics, machine learning, and resources

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