Book Image

Graph Data Modeling in Python

By : Gary Hutson, Matt Jackson
Book Image

Graph Data Modeling in Python

By: Gary Hutson, Matt Jackson

Overview of this book

Graphs have become increasingly integral to powering the products and services we use in our daily lives, driving social media, online shopping recommendations, and even fraud detection. With this book, you’ll see how a good graph data model can help enhance efficiency and unlock hidden insights through complex network analysis. Graph Data Modeling in Python will guide you through designing, implementing, and harnessing a variety of graph data models using the popular open source Python libraries NetworkX and igraph. Following practical use cases and examples, you’ll find out how to design optimal graph models capable of supporting a wide range of queries and features. Moreover, you’ll seamlessly transition from traditional relational databases and tabular data to the dynamic world of graph data structures that allow powerful, path-based analyses. As well as learning how to manage a persistent graph database using Neo4j, you’ll also get to grips with adapting your network model to evolving data requirements. By the end of this book, you’ll be able to transform tabular data into powerful graph data models. In essence, you’ll build your knowledge from beginner to advanced-level practitioner in no time.
Table of Contents (16 chapters)
Part 1: Getting Started with Graph Data Modeling
Part 2: Making the Graph Transition
Part 3: Storing and Productionizing Graphs
Part 4: Graphing Like a Pro

Effectively evolving with graph schema design

When planning a schema in the previous chapters, we assumed that our graph data model would not change. In practice, this is rarely the case. Data requirements can change regularly with the needs of a solution or business, and it is important to consider this in advance. We saw in the last section that it is often more simple to change the schema of a graph than the structure of a relational database.

On the other hand, when a graph database becomes a key part of the tech stack that underpins a valuable system, we would not want to allow drastic shifts in structure and schema, especially those that may disrupt a live service. We have to strike a balance between taking advantage of the mutable structure and schema of a graph database and setting sensible constraints that ensure data is as expected.

For the rest of the chapter, let’s consider a new example, using data from Twitter. Twitter, like many other social media platforms...