1. Graph Databases in Action: Examples in Gremlin
This book, authored by Josh Perryman and Dave Bechberger, is a comprehensive guide that dives deep into the world of graph databases through the use of Gremlin. It illuminates the unique features and performance advantages of graph databases with practical examples that empower readers to grasp the practical applications in real-world scenarios. If you’re looking to understand how to leverage graph databases effectively, then this book is a must-read.
2. Graph Data Modeling in Python: A Practical Guide
Gary Hutson and Matt Jackson present this invaluable resource for data scientists looking to curate and analyze data using graphs in Python. The book combines theory with many hands-on projects that guide readers through the intricate process of data modeling. It’s an essential guide that ensures you can transform data into actionable insights, making it a cornerstone for anyone looking to upscale their data skills.
3. Exploring Graphs with Elixir
Tony Hammond provides a unique perspective by exploring graph databases through Elixir, a functional programming language. This book shows how to connect data using native graph libraries and emphasizes performance and usability. The hands-on approach helps demystify graph processing and will appeal to developers seeking to expand their toolset for efficient data management.
4. Graph Data Science with Python and Neo4j
Timothy Eastridge’s work is a beacon for those interested in integrating Python with the Neo4j platform for data visualization and analysis. This book promises hands-on projects that demonstrate how to bring powerful data science strategies to life using graphs. For anyone serious about enhancing their data science methodologies, this book serves as a guide for leveraging graphs in enterprise strategies.
5. Graph Data Processing with Cypher
Written by Ravindranatha Anthapu, this book focuses on building graph traversal queries using the Cypher syntax on Neo4j. It offers practical guidance that is rooted in real-world applications of graph data processing, enabling readers to tackle complex data queries efficiently. If you want to master Cypher for your data processing needs, this is the book you need.
6. Graph Database Modeling: 2nd Edition
Ajit Singh’s 2nd Edition of Graph Database Modeling offers updated insights and methods for effective database design using graph techniques. Priced affordably, this book is perfect for beginners and experienced professionals alike as it provides a solid foundation for creating efficient models that leverage the strengths of graph databases.
7. The Essential Criteria of Graph Databases
Ricky Sun’s essential guide delves into the key criteria that determine the effectiveness of graph databases. With its advanced insights, this book is a necessary addition for database architects and data scientists looking to implement or improve upon their graph database strategies, ensuring they achieve optimized performance and reliability.
8. Graph Database Modeling With Neo4j: 2nd Edition
Another essential work from Ajit Singh, this edition focuses specifically on modeling with Neo4j, one of the leading graph database systems. This guide is practical and user-friendly, making it ideal for anyone from newcomers grasping the basics to seasoned pros seeking to refine their modeling skills.
9. Introduction to Graph Theory
Richard J. Trudeau’s classic book provides a thorough understanding of graph theory from a mathematical perspective. It stands out not only as a textbook but also as a reference point for anyone entering the world of data structures or computer science. This foundation is increasingly relevant in today’s data-centric world.
10. Graph Data Science with Neo4j: Learn how to use Neo4j 5
Scifo Estelle’s guide on how to utilize Neo4j version 5 with the Graph Data Science library is perfect for tech enthusiasts who want to cultivate their data skills. It covers all the updates and features offered in the new release and aligns them with practical projects, making this resource indispensable for your data science toolkit.
