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

Part 3: Storing and Productionizing Graphs

Building production-grade systems is a breeze with this part. We start by working with graph databases and expose the use of Neo4j to store our databases for fast and efficient processing and information retrieval. Once we have the data stored, we then move on to create a route optimization solution, using the flexibility of the node to edge connections, and the speed at which they can be queried in a graph database.

Logically, the transition is then to move on to designing production-quality pipelines that can be evolved effectively to meet the change in the underlying data. The focus of these sections is to make Python work in harmony with Neo4j and to make sure we have built pipelines along the way that can be changed, morphed, and evolved over time.

This part has the following chapters: