The Practitioner's Guide to Graph Data: Applying Graph Thinking and Graph Technologies to Solve Complex Problems

From the Preface

This book aims to teach you two things. First, we will teach you about graph thinking and the graph mindset through asking questions and reasoning about data. Second, we will walk you through writing code that solves the most common, complex graph problems.

These new concepts are intertwined within the tasks commonly performed across a few different engineering functions.

Data engineers and architects sit at the heart of transitioning an idea from development into production. We organized this book to show you how to resolve common assumptions that can occur when moving from development into production with graph data and graph tools. Another benefit to the data engineer or data architect will be learning the world of possibilities that come from understanding graph thinking. Synthesizing the breadth of problems that can be solved with graph data will also help you invent new patterns for their use in production applications.

Data scientists and data analysts may most benefit from reasoning about how to use graph data to answer interesting questions. All the examples throughout this text were constructed to apply a query-first approach to graph data.

A secondary benefit for a data scientist or analyst will be to understand the complexity of using distributed graph data within a production application. We teach and build upon the common development pitfalls and their production resolution processes throughout the book so that you can formulate new types of problems to solve.

Data scientists and data analysts may most benefit from reasoning about how to use graph data to answer interesting questions. All the examples throughout this text were constructed to apply a query-first approach to graph data. A secondary benefit for a data scientist or analyst will be to understand the complexity of using distributed graph data within a production application. We teach and build upon the common development pitfalls and their production resolution processes throughout the book so that you can formulate new types of problems to solve.

Computer scientists will learn how to use techniques in functional programming and distributed systems to query and reason about graph data. We will outline fundamental approaches to procedurally traversing graph data and step through their application with graph tools. Along the way we will learn about distributed technologies, too.

We will be working within the intersection of graph data and distributed, complex problems; a fascinating combination of engineering topics with something to learn for any technologist.