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

Designing a schema and pipeline

Let’s tackle each of the elements we need to set up one at a time. For our backend system, we will need a graph database, so the first stage is setting up a new, blank Neo4j database.

Setting up a new database

As we did in Chapter 5, Working with Graph Databases, let’s start up Neo4j Desktop. Once we have loaded up the desktop, we need to follow these steps to add a new database:

  1. In the main Neo4j window, select Add, and choose Local DBMS.
  2. Choose a name for the new database, for example, Store DB.
  3. Use a generic password too, for example, testpython. We will need to use this password in open code in this example, so make sure not to use a sensitive keyphrase. In a real production system, any authentication to this database required by third-party scripts would likely use a password secret system, to prevent exposure of this password in plain text and code.
  4. Next, click Create, and wait for the new graph database...