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

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

    Graph Data Modeling in Python
    by Gary Hutson and Matt Jackson

    English | 2023 | ISBN: 1804618039 | 236 pages | True PDF EPUB | 9.45 MB




    Learn how to transform, store, evolve, refactor, model, and create graph projections using the Python programming language

    Purchase of the print or Kindle book includes a free PDF eBook
    Key Features

    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

    Book 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.
    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

    Who this book is 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.
    Table of Contents

    Introducing Graphs in the Real World
    Working with Graph Data Models
    Data Model Transformation – Relational to Graph Databases
    Building a Knowledge Graph
    Working with Graph Databases
    Pipeline Development
    Refactoring and Evolving Schemas
    Perfect Projections
    Common Errors and Debugging



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