This book guides the reader through designing, implementing, and harnessing a variety of graph data models using the popular open source Python libraries NetworkX and igraph. Gary Hutson and Matt Jackson provide practical use cases and examples to illustrate how to design optimal graph models capable of supporting a wide range of queries and features. In addition to showing how to manage a persistent graph database using Neo4j, the book also looks at adapting your network model to evolving data requirements.

Author: Gary Hutson and Matt Jackson
Publisher: Packt Publishing
Date: June 2023
Pages: 236
ISBN: 978-1804618035
Print: ‎ 1804618039
Kindle: B0C9FMYBYQ
Audience: Database developers
Level: Intermediate
Category: Data Science

Topics include:

• Design graph data models and master schema design best practices
• Work with the NetworkX and igraph frameworks in Python Store, query, ingest, and refactor graph data
• Store your graphs in memory with Neo4j
• Build and work with projections and put them into practice
• Refactor schemas and learn tactics for managing an evolved graph data model

For recommendations of Big Data books see Reading Your Way Into Big Data in our Programmer's Bookshelf section.

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