Graph Databases, 2nd Edition

Preface

Graph databases address one of the great macroscopic business trends of today: leveraging complex and dynamic relationships in highly connected data to generate insight and competitive advantage. Whether we want to understand relationships between customers, elements in a telephone or data center network, entertainment producers and consumers, or genes and proteins, the ability to understand and analyze vast graphs of highly connected data will be key in determining which companies outperform their competitors over the coming decade.

For data of any significant size or value, graph databases are the best way to represent and query connected data. Connected data is data whose interpretation and value requires us first to understand the ways in which its constituent elements are related. More often than not, to generate this understanding, we need to name and qualify the connections between things.

Although large corporations realized this some time ago and began creating their own proprietary graph processing technologies, we’re now in an era where that technology has rapidly become democratized. Today, general-purpose graph databases are a reality, enabling mainstream users to experience the benefits of connected data without having to invest in building their own graph infrastructure.

What’s remarkable about this renaissance of graph data and graph thinking is that graph theory itself is not new. Graph theory was pioneered by Euler in the 18th century, and has been actively ...