Graph Data Modeling in Python (Packt)
Wednesday, 19 July 2023

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.

<ASIN:‎ 1804618039 >


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.


For more Book Watch just click.

Book Watch is I Programmer's listing of new books and is compiled using publishers' publicity material. It is not to be read as a review where we provide an independent assessment. Some, but by no means all, of the books in Book Watch are eventually reviewed.

To have new titles included in Book Watch contact

Follow @bookwatchiprog on Twitter or subscribe to I Programmer's Books RSS feed for each day's new addition to Book Watch and for new reviews.




Learn Enough JavaScript to Be Dangerous

Author: Michael Hartl
Publisher: Addison-Wesley
Date: June 2022
Pages: 304
ISBN: 978-0137843749
Print: 0137843747
Kindle: B09RDSVV7N
Audience: Would-be JavaScript developers
Rating: 2
Reviewer: Mike James
To be dangerous? Is this a good ambition?

Deep Learning (No Starch Press)

Author: Andrew Glassner
Publisher: No Starch Press
Date: July 2021
Pages: 750
ISBN: 978-1718500723
Print: 1718500726
Kindle: ‎ B085BVWXNS
Audience: Developers interested in deep learning
Rating: Mike James
Reviewer: 5
A book on deep learning wtihout an equation in sight?

More Reviews