Matthias Broecheler - The Practitioners Guide to Graph Data

by Denise Koessler Gosnell and Matthias Broecheler

Copyright 2020 Denise Gosnell and Matthias Broecheler . All rights reserved.

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978-1-492-04407-9

[LSI]

Think about the last time you searched for someone on a social media platform.

What did you look at on the results page?

Most likely, you started scanning down the names in the list of profile results. And you probably spent most of your time inspecting the shared friends section to understand how you knew someone.

Our innate human behavior of reasoning about our shared friends on social media is what inspired us to write this book. Though, our shared inspiration generated two very different reasons behind why we wrote this book.

First, have you ever stopped to think about how an app creates the shared friends section?

The engineering required to deliver your shared friends in search results creates an intricate orchestration of tools and data to solve an extremely complex, distributed problem. We have either built those sections or created the tools that deliver them. Our passion for understanding and teaching others from our collective experiences is the first reason we chose to write this book together.

The second reason is that anyone who uses social media intuitively derives personal context directly from the shared friends section. This process of reasoning and thinking about relationships within data is called graph thinking, and that is what we name the human approach to understanding life through connected data.

How did we all learn to do this?

There wasnt a specific point in time when we all were taught this skill. Processing relationships among people, places, or things is just how we think.

It is the ease with which people infer context from relationships, be it in real life or from data, that has ignited the wave of graph thinking.

And when it comes to understanding graph thinking, most people fall into one of two camps: those who think graphs are about bar charts, or those who think graphs are way too complicated. Either way, these thought processes apply legacy approaches to thinking about data and technology. The problem is that the art of the possible has changed, our tools have improved, and there are new lessons to learn.

We believe that graphs are powerful and deployable. Graph technology can make you more productive; we have worked with teams that told us so.

This book brings these two mindsets together.

Graph thinking closes the gap between how we humans operate/see/live and how we use data to inform a decision. Imagine seeing your whole world as a spreadsheet with rows and columns of data and trying to make sense of it all. For the majority of us, the exercise is unnatural and counterproductive.

This is because relationships are how people navigate and reason about life. It is computers that need databases and operate in the world of rows and columns of data.

Graph thinking is a way to solve complex problems by taking a relationship-centric approach. Graph technology bridges the gap between relationships and the linear memory constraints of modern computer infrastructure.

As more people learn how to build with graph technology by applying graph thinking, imagine what the next wave of innovation will bring.