Graph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life through relationships. This practical guide demonstrates how graph data brings these two approaches together. By working with concepts from graph theory, database schema, distributed systems, and data analysis, you’ll arrive at a unique intersection known as graph thinking.

Authors Denise Koessler Gosnell and Matthias Broecheler show data engineers, data scientists, and data analysts how to solve complex problems with graph databases. You’ll explore templates for building with graph technology, along with examples that demonstrate how teams think about graph data within an application.
 

• •  Build an example application architecture with relational and graph technologies
• •  Use graph technology to build a Customer 360 application, the most popular graph data pattern today
• •  Dive into hierarchical data and troubleshoot a new paradigm that comes from working with graph data
• •  Find paths in graph data and learn why your trust in different paths motivates and informs your preferences
• •  Use collaborative filtering to design a Netflix-inspired recommendation system
  
  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 analysis 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.

Goals of This Book

The first goal of this book is to create a new foundation that exists at a very diverse intersection. We will be working with concepts from graph theory, database schema, distributed systems, data analysis, and many other fields. This unique intersection forms what we refer to in this book as graph thinking. A new application domain requires new terms, examples, and techniques. This book serves as your foundation for understanding this emerging field.
 

From the past decade of graph technology emerged a common set of patterns for using graph data in production applications. The second goal of this book is to teach you those patterns. We define, illustrate, build, and implement the most popular ways teams use graph technology to solve complex problems. After studying this book, you will have a set of templates for building with graph technology to solve this common set of problems.
 

The third goal of this book is to transform how you think. Understanding and applying graph data to your problem introduces a paradigm shift into your thought processes. Through many upcoming examples, we aim to teach you the common ways that others think and reason about graph data within an application. This book teaches you what you need to know to apply graph thinking to a technology decision.

About the Author

Dr. Denise Gosnell’s passion for examining, applying, and evangelizing the applications of graph data was ignited during her apprenticeship under Dr. Teresa Haynes and Dr. Debra Knisley during her first NSF Fellowship. This group’s work was one of the earliest applications of neural networks and graph theoretic structure in predictive computational biology. Since then, Dr. Gosnell has built, published, patented, and spoke on dozens of topics related to graph theory, graph algorithms, graph databases, and applications of graph data across all industry verticals.
 

Currently, Dr. Gosnell is with DataStax where she aspires to build upon her experiences as a data scientist and graph architect. Prior to her role with DataStax, she built software solutions for and spoke at over a dozen conferences on permissioned blockchains, machine learning applications of graph analytics, and data science within the healthcare industry.

Dr. Matthias Broecheler is a technologist and entrepreneur with substantial research anddevelopment experience who is focused on disruptive software technologies and understanding complex systems. Dr. Broecheler’s is known as an industry expert in graph databases, relational machine learning, and big data analysis in general. He is a practitioner of lean methodologies and experimentation to drive continuous improvement. Dr. Broecheler is the inventor of the Titan graph database and a founder of Aurelius.