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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

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In Chapter 2, Working with Graph Data Models, we showed how tabular node and edge data can be used to model and construct a graph with Python. We can use this graph to ask questions that would be difficult and inefficient using the original tabular data, thus demonstrating the power of a graph model.

In this chapter, we will be looking more closely, with examples, at the issues that arise when answering graph-like questions using a relational database. In Chapter 1, Introducing Graphs in the Real World, we touched on how path-based operations are inefficient when using tabular data, due to the requirement for repeated table joins.

However, in real situations, data is often not in the form of node properties and edge lists. A huge amount of data, across every sector, is stored in the form of relational data tables. Relational data is often stored and accessed using SQL, or a SQL-like storage system and query language (for example, MySQL).

In this...

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