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

Moving to ingestion pipelines

In this chapter, we have created a static Neo4j graph, queried and analyzed it, and updated its features. This type of solution might be used in a production environment, where it might be used to serve up results in an application.

However, there are elements of this type of process that we are yet to cover. In a production system, nodes and edges may be read and written to a graph database regularly, often in small batches. Complicated processing might take place outside of Neo4j, before data is ingested, using Python or other languages. Think about how many options a real travel optimization or recommendation service actually offers – each of these has to be embedded into a graph database pipeline.

In Chapter 6, Pipeline Development, we will look at an example of a complex graph data pipeline and explore how to make it reliable and efficient.