Graph Data Modeling in Python: A practical guide to curating, analyzing, and modeling data with graphs

Gary Hutson

Matt Jackson

€23.99 ~~€26.99~~

4.8 (6 Ratings)

eBook Jun 2023 236 pages 1st Edition

Gary Hutson

Matt Jackson

€23.99 ~~€26.99~~

4.8 (6 Ratings)

eBook Jun 2023 236 pages 1st Edition

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Key benefits

Transform relational data models into graph data model while learning key applications along the way
Discover common challenges in graph modeling and analysis, and learn how to overcome them
Practice real-world use cases of community detection, knowledge graph, and recommendation network

Description

Graphs have become increasingly integral to powering the products and services we use in our daily lives, driving social media, online shopping recommendations, and even fraud detection. With this book, you’ll see how a good graph data model can help enhance efficiency and unlock hidden insights through complex network analysis. Graph Data Modeling in Python will guide you through designing, implementing, and harnessing a variety of graph data models using the popular open source Python libraries NetworkX and igraph. Following practical use cases and examples, you’ll find out how to design optimal graph models capable of supporting a wide range of queries and features. Moreover, you’ll seamlessly transition from traditional relational databases and tabular data to the dynamic world of graph data structures that allow powerful, path-based analyses. As well as learning how to manage a persistent graph database using Neo4j, you’ll also get to grips with adapting your network model to evolving data requirements. By the end of this book, you’ll be able to transform tabular data into powerful graph data models. In essence, you’ll build your knowledge from beginner to advanced-level practitioner in no time.

Who is this book for?

If you are a data analyst or database developer interested in learning graph databases and how to curate and extract data from them, this is the book for you. It is also beneficial for data scientists and Python developers looking to get started with graph data modeling. Although knowledge of Python is assumed, no prior experience in graph data modeling theory and techniques is required.

What you will learn

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Jacob Silva Mar 07, 2024

Great book! It quickly teaches a beginner about graph data with easy-to-understand content, hands-on exercises, and provided source code. Then, it touches on advanced topics like projections on graph data. I am happy we picked this book for our Tech Book Club

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Amanda Fetch Sep 26, 2023

As someone who is currently working with a knowledge graph based solution within an organization, it is imperative that I find solid resources to cover both the basics and more advanced concepts of graph data modeling. "Graph Data Modeling in Python" starts with the basics of graph structures and their real-world applications, then the book systematically covers practical use-cases from making recommendations using Python to designing complex pipelines with Neo4j and Cypher. Its in-depth coverage on transforming relational databases to graph databases, building knowledge graphs, and addressing common errors I found very useful. A perfect blend of theory and hands-on examples makes it an indispensable resource for anyone venturing into the world of graph databases. Highly recommended for both beginners and seasoned professionals!

Jacob Vandergriff Sep 28, 2023

Graph Data Modeling in Python provides a great introduction for those familiar with python and relational databases.The flow from relational databases, to python graph modeling, to scaling up to Neo4j databases was exceptional and can get any beginner to a graph developer.

Om S Jul 05, 2023

The book begins by highlighting the growing importance of graphs in our daily lives, driving various applications such as social media, recommendation systems, and fraud detection. Readers are introduced to the benefits of graph data models, which enhance efficiency and uncover hidden insights through complex network analysis.With a focus on practical use cases, readers learn how to design optimal graph models capable of supporting a wide range of queries and features. The transition from traditional relational databases to dynamic graph data structures is seamlessly addressed, empowering readers to unlock the power of path-based analyses. The book also covers working with Neo4j for persistent graph database management.By the end of the book, readers will possess the skills to transform tabular data into powerful graph data models, ranging from beginner to advanced-level proficiency. They will have a deep understanding of schema design best practices, proficiency in NetworkX and igraph frameworks, and the ability to store, query, ingest, and refactor graph data."Graph Data Modeling in Python: A Practical Guide to Curating, Analyzing, and Modeling Data with Graphs" is a must-read for those interested in graph databases and data modeling. The book caters to individuals with prior knowledge of Python, while also being accessible to beginners in graph data modeling. Its practical approach, real-world examples, and coverage of common errors and debugging make it an essential companion for those looking to extract valuable insights from graph data.

SEAN W. GRANT Aug 18, 2023

This was a good book to read that helped in understanding how to build a graph from various sources (rdbms tables, NLP outputs). It showed how you can use some common python libraries (NLTK, Spacy) to process some data and create a graph from it. There were a couple chapters that I especially liked: Chapter 4 and 7.Chapter 4- Building Knowledge Graphs: Author does a great job of explaining what a knowledge graph is, how to design one and then walk through how to build one. Knowledge graphs are becoming a popular topic and structure that businesses are using to provide better understanding and this chapter will help you understand how you can start looking at your data to build a knowledge graph.Chapter 7-Refactoring and Evolving Schemas: I found this to be a great explanation of power of the graph database and how it can adjust fast to your needs without causing a problem with live data. The hands-on examples were great at showing how easy it is to change the schema in the Neo4j database.Overall this was a great book for getting your hands dirty on designing, understand and building graphs that will help you think about how connected data really is.

Graph Data Modeling in Python: A practical guide to curating, analyzing, and modeling data with graphs

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