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!
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
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
