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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
Author Profile Icon Matt Jackson
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

Who this book is for

The primary aim of this book is to assist existing Python developers of an intermediate level who may have the ambition of getting into graph data modeling. Or, if you are a database developer or IT professional, then the graph database section may give you insights into how graph databases are different from traditional databases.

In essence, this book is targeted at anyone who loves coding in Python and wants to learn more about how to build graph data pipelines, how to ingest and clean data, various ways to store graph data relationships, how to conduct analytical techniques such as community detection and recommendation engine creation, and how to use Cypher to store these relationships and then query the in-memory graph with Cypher.

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