The Practitioner’s Guide to Graph Data 1st edition by Denise Koessler Gosnell, Matthtionias Broecheler ISBN 1492044075 978-1492044079

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ISBN 10: 1492044075
ISBN 13: 978-1492044079
Author: Denise Koessler Gosnell, Matthtionias Broecheler

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.

The Practitioner’s Guide to Graph Data 1st Table of contents:

1. Graph Thinking

Why Now? Putting Database Technologies in Context

1960s–1980s: Hierarchical Data

1980s–2000s: Entity-Relationship

2000s–2020s: NoSQL

2020s–?: Graph

What Is Graph Thinking?

Complex Problems and Complex Systems

Complex Problems in Business

Making Technology Decisions to Solve Complex Problems

So You Have Graph Data. What’s Next?

Seeing the Bigger Picture

Getting Started on Your Journey with Graph Thinking

2. Evolving from Relational to Graph Thinking

Chapter Preview: Translating Relational Concepts to Graph Terminology

Relational Versus Graph: What’s the Difference?

Data for Our Running Example

Relational Data Modeling

Entities and Attributes

Building Up to an ERD

Concepts in Graph Data

Fundamental Elements of a Graph

Adjacency

Neighborhoods

Distance

Degree

The Graph Schema Language

Vertex Labels and Edge Labels

Properties

Edge Direction

Self-Referencing Edge Labels

Multiplicity of Your Graph

Full Example Graph Model

Relational Versus Graph: Decisions to Consider

Data Modeling

Understanding Graph Data

Mixing Database Design with Application Purpose

Summary

3. Getting Started: A Simple Customer 360

Chapter Preview: Relational Versus Graph

The Foundational Use Case for Graph Data: C360

Why Do Businesses Care About C360?

Implementing a C360 Application in a Relational System

Data Models

Relational Implementation

Example C360 Queries

Implementing a C360 Application in a Graph System

Data Models

Graph Implementation

Example C360 Queries

Relational Versus Graph: How to Choose?

Relational Versus Graph: Data Modeling

Relational Versus Graph: Representing Relationships

Relational Versus Graph: Query Languages

Relational Versus Graph: Main Points

Summary

Why Not Relational?

Making a Technology Choice for Your C360 Application

4. Exploring Neighborhoods in Development

Chapter Preview: Building a More Realistic Customer 360

Graph Data Modeling 101

Should This Be a Vertex or an Edge?

Lost Yet? Let Us Walk You Through Direction

A Graph Has No Name: Common Mistakes in Naming

Our Full Development Graph Model

Before We Start Building

Our Thoughts on the Importance of Data, Queries, and the End User

Implementation Details for Exploring Neighborhoods in Development

Generating More Data for Our Expanded Example

Basic Gremlin Navigation

Advanced Gremlin: Shaping Your Query Results

Shaping Query Results with the project(), fold(), and unfold() Steps

Removing Data from the Results with the where(neq()) Pattern

Planning for Robust Result Payloads with the coalesce() Step

Moving from Development into Production

5. Exploring Neighborhoods in Production

Chapter Preview: Understanding Distributed Graph Data in Apache Cassandra

Working with Graph Data in Apache Cassandra

The Most Important Topic to Understand About Data Modeling: Primary Keys

Partition Keys and Data Locality in a Distributed Environment

Understanding Edges, Part 1: Edges in Adjacency Lists

Understanding Edges, Part 2: Clustering Columns

Understanding Edges, Part 3: Materialized Views for Traversals

Graph Data Modeling 201

Finding Indexes with an Intelligent Index Recommendation System

Production Implementation Details

Materialized Views and Adding Time onto Edges

Our Final C360 Production Schema

Bulk Loading Graph Data

Updating Our Gremlin Queries to Use Time on Edges

Moving On to More Complex, Distributed Graph Problems

Our First 10 Tips to Get from Development to Production

6. Using Trees in Development

Chapter Preview: Navigating Trees, Hierarchical Data, and Cycles

Seeing Hierarchies and Nested Data: Three Examples

Hierarchical Data in a Bill of Materials

Hierarchical Data in Version Control Systems

Hierarchical Data in Self-Organizing Networks

Why Graph Technology for Hierarchical Data?

Finding Your Way Through a Forest of Terminology

Trees, Roots, and Leaves

Depth in Walks, Paths, and Cycles

Understanding Hierarchies with Our Sensor Data

Understand the Data

Conceptual Model Using the GSL Notation

Implement Schema

Before We Build Our Queries

Querying from Leaves to Roots in Development

Where Has This Sensor Sent Information To?

From This Sensor, What Was Its Path to Any Tower?

From Bottom Up to Top Down

Querying from Roots to Leaves in Development

Setup Query: Which Tower Has the Most Sensor Connections So That We Could Explore It for Our Example?

Which Sensors Have Connected Directly to Georgetown?

Find All Sensors That Connected to Georgetown

Depth Limiting in Recursion

Going Back in Time

7. Using Trees in Production

Chapter Preview: Understanding Branching Factor, Depth, and Time on Edges

Understanding Time in the Sensor Data

Final Thoughts on Time Series Data in Graphs

Understanding Branching Factor in Our Example

What Is Branching Factor?

How Do We Get Around Branching Factor?

Production Schema for Our Sensor Data

Querying from Leaves to Roots in Production

Where Has This Sensor Sent Information to, and at What Time?

From This Sensor, Find All Trees up to a Tower by Time

From This Sensor, Find a Valid Tree

Advanced Gremlin: Understanding the where().by() Pattern

Querying from Roots to Leaves in Production

Which Sensors Have Connected to Georgetown Directly, by Time?

What Valid Paths Can We Find from Georgetown Down to All Sensors?

Applying Your Queries to Tower Failure Scenarios

Applying the Final Results of Our Complex Problem

Seeing the Forest for the Trees

8. Finding Paths in Development

Chapter Preview: Quantifying Trust in Networks

Thinking About Trust: Three Examples

How Much Do You Trust That Open Invitation?

How Defensible Is an Investigator’s Story?

How Do Companies Model Package Delivery?

Fundamental Concepts About Paths

Shortest Paths

Depth-First Search and Breadth-First Search

Learning to See Application Features as Different Path Problems

Finding Paths in a Trust Network

Source Data

A Brief Primer on Bitcoin Terminology

Creating Our Development Schema

Loading Data

Exploring Communities of Trust

Understanding Traversals with Our Bitcoin Trust Network

Which Addresses Are in the First Neighborhood?

Which Addresses Are in the Second Neighborhood?

Which Addresses Are in the Second Neighborhood, but Not the First?

Evaluation Strategies with the Gremlin Query Language

Pick a Random Address to Use for Our Example

Shortest Path Queries

Finding Paths of a Fixed Length

Finding Paths of Any Length

Augmenting Our Paths with the Trust Scores

Do You Trust This Person?

9. Finding Paths in Production

Chapter Preview: Understanding Weights, Distance, and Pruning

Weighted Paths and Search Algorithms

Shortest Weighted Path Problem Definition

Shortest Weighted Path Search Optimizations

Normalization of Edge Weights for Shortest Path Problems

Normalizing the Edge Weights

Updating Our Graph

Exploring the Normalized Edge Weights

Some Thoughts Before Moving On to Shortest Weighted Path Queries

Shortest Weighted Path Queries

Building a Shortest Weighted Path Query for Production

Weighted Paths and Trust in Production

10. Recommendations in Development

Chapter Preview: Collaborative Filtering for Movie Recommendations

Recommendation System Examples

How We Give Recommendations in Healthcare

How We Experience Recommendations in Social Media

How We Use Deeply Connected Data for Recommendations in Ecommerce

An Introduction to Collaborative Filtering

Understanding the Problem and Domain

Collaborative Filtering with Graph Data

Recommendations via Item-Based Collaborative Filtering with Graph Data

Three Different Models for Ranking Recommendations

Movie Data: Schema, Loading, and Query Review

Data Model for Movie Recommendations

Schema Code for Movie Recommendations

Loading the Movie Data

Neighborhood Queries in the Movie Data

Tree Queries in the Movie Data

Path Queries in the Movie Data

Item-Based Collaborative Filtering in Gremlin

Model 1: Counting Paths in the Recommendation Set

Model 2: NPS-Inspired

Model 3: Normalized NPS

Choosing Your Own Adventure: Movies and Graph Problems Edition

11. Simple Entity Resolution in Graphs

Chapter Preview: Merging Multiple Datasets into One Graph

Defining a Different Complex Problem: Entity Resolution

Seeing the Complex Problem

Analyzing the Two Movie Datasets

MovieLens Dataset

Kaggle Dataset

Development Schema

Matching and Merging the Movie Data

Our Matching Process

Resolving False Positives

False Positives Found in the MovieLens Dataset

Additional Errors Discovered in the Entity Resolution Process

Final Analysis of the Merging Process

The Role of Graph Structure in Merging Movie Data

12. Recommendations in Production

Chapter Preview: Understanding Shortcut Edges, Precomputation, and Advanced Pruning Techniques

Shortcut Edges for Recommendations in Real Time

Where Our Development Process Doesn’t Scale

How We Fix Scaling Issues: Shortcut Edges

Seeing What We Designed to Deliver in Production

Pruning: Different Ways to Precompute Shortcut Edges

Considerations for Updating Your Recommendations

Calculating Shortcut Edges for Our Movie Data

Breaking Down the Complex Problem of Precalculating Shortcut Edges

Addressing the Elephant in the Room: Batch Computation

Production Schema and Data Loading for Movie Recommendations

Production Schema for Movie Recommendations

Production Data Loading for Movie Recommendations

Recommendation Queries with Shortcut Edges

Confirming Our Edges Loaded Correctly

Production Recommendations for Our User

Understanding Response Time in Production by Counting Edge Partitions

Final Thoughts on Reasoning About Distributed Graph Query Performance

13. Epilogue

Where to Go from Here?

Graph Algorithms

Distributed Graphs

Graph Theory

Network Theory

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Tags: Denise Koessler Gosnell, Matthtionias Broecheler, The Practitioner’s, Graph Data