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Graph Data Modeling in Python

You're reading from   Graph Data Modeling in Python A practical guide to curating, analyzing, and modeling data with graphs

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Product type Paperback
Published in Jun 2023
Publisher Packt
ISBN-13 9781804618035
Length 236 pages
Edition 1st Edition
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Authors (2):
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Gary Hutson Gary Hutson
Author Profile Icon Gary Hutson
Gary Hutson
Matt Jackson Matt Jackson
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Matt Jackson
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Table of Contents (16) Chapters Close

Preface 1. Part 1: Getting Started with Graph Data Modeling
2. Chapter 1: Introducing Graphs in the Real World FREE CHAPTER 3. Chapter 2: Working with Graph Data Models 4. Part 2: Making the Graph Transition
5. Chapter 3: Data Model Transformation – Relational to Graph Databases 6. Chapter 4: Building a Knowledge Graph 7. Part 3: Storing and Productionizing Graphs
8. Chapter 5: Working with Graph Databases 9. Chapter 6: Pipeline Development 10. Chapter 7: Refactoring and Evolving Schemas 11. Part 4: Graphing Like a Pro
12. Chapter 8: Perfect Projections 13. Chapter 9: Common Errors and Debugging 14. Index 15. Other Books You May Enjoy

Common Errors and Debugging

In this chapter, we will be working through common errors that you will be presented with as a newly qualified graph data scientist or engineer (which you will be when you have finished reading this book). The focus will be on how to debug graph issues. This will lead on to common issues and how to get around them when working in igraph. Following on from this, we will look at common Neo4j issues, as we have been working igraph, Python, and Neo4j for a large proportion of this book; therefore, it is useful to know how to get around graph database issues as well. We will also touch on how to modify your igraph and Neo4j scripts to achieve the best performance.

There are many examples under each of these sections, with use cases of how and why these issues may present themselves. Therefore, you will be perfectly prepared, after reading this chapter, to debug and solve these common issues in your projects. In the upcoming sections, you will delve into common...

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