1. Graph Databases: Applications on Social Media Analytics and Smart Cities
This groundbreaking book illustrates the transformative capabilities of graph databases in the spheres of social media analytics and smart cities. Authored by Christos Tjortjis, it provides a comprehensive exploration of innovative applications and insights drawn from real-world scenarios, making it a must-read for professionals looking to leverage data in impactful ways. With social media continuously evolving, understanding graph databases is not just beneficial; it’s essential for anyone working within these dynamic fields.
2. Graph Databases
Renowned authors Ian Robinson, Jim Webber, and Emil Eifrem join forces in this essential text that demystifies the world of graph databases. Their engaging writing style makes complex concepts accessible to a variety of readers—from novices to seasoned database administrators. The book leverages practical examples to illustrate how graph databases changes the landscape of data management, making it an unavoidable reference for anyone pursuing greenfield projects or transitioning from relational databases.
3. Graph Data Science with Python and Neo4j: Hands-on Projects on Python and Neo4j Integration for Data Visualization and Analysis Using Graph Data
This hands-on guide by Timothy Eastridge explores the powerful integration of Python with Neo4j for unparalleled data visualization and analysis. It offers practical projects designed to solidify the reader’s understanding of utilizing graph data effectively. The book is not just theoretical; it’s a practical toolkit for data scientists eager to unlock the full potential of graph algorithms within their Python environments, making it an essential read for aspiring data scientists.
4. Exploring Graphs with Elixir: Connect Data with Native Graph Libraries and Graph Databases
In this insightful book, Tony Hammond covers how to leverage Elixir’s capabilities to connect and manage data using graph libraries. This book stands out by offering a functional programming perspective, making it an invaluable asset for developers looking to apply functional paradigms to contemporary data challenges. The practical examples vividly illustrate the integration of Elixir with graph databases, highlighting its applicability in modern software development.
5. Neo4j Graph Database: 2nd Edition
Ajit Singh and Dr. Sheela Hundekari deliver an updated exploration of Neo4j, designed to equip readers with practical knowledge for building robust graph databases. This second edition dives deeper into advanced features, making it an ideal resource for those seeking to enhance their understanding of graph technologies. The clarity of explanations, paired with practical demonstrations, ensures that readers can readily implement what they learn in their own projects, making it a must-have for any serious database administrator.
6. Graph Data Modeling in Python: A practical guide to curating, analyzing, and modeling data with graphs
Gary Hutson and Matt Jackson deliver insights into graph data modeling tailored for Python developers. This practical guide showcases real-life use cases that demonstrate how to curate and analyze data effectively using graphs. By reading this book, you will acquire the skills to architect sophisticated graph structures in your applications, making it a critical addition to your data science toolkit.
7. Knowledge Graphs: Fundamentals, Techniques, and Applications
Mayank Kejriwal, Craig A. Knoblock, and Pedro Szekely provide a fantastic overview of knowledge graphs, laying down essential fundamentals, techniques, and real-world applications. This book acts as a guide for both newcomers and seasoned professionals in the area of machine learning and artificial intelligence, showcasing how knowledge graphs facilitate interoperability among heterogeneous datasets. Understanding knowledge graphs enhances a reader’s potential and applicability in various data-centric roles.
8. Graph Data Science with Neo4j: Learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project
Estelle Scifo’s comprehensive book on applying Neo4j 5 and its Graph Data Science library delivers essential insights for those interested in implementing cutting-edge graph algorithms. This book equips readers with the knowledge to transform raw data into incredible insights through a hands-on learning approach, making it indispensable for data scientists aiming to utilize Neo4j for real-world applications.
9. Graph Data Processing with Cypher: A practical guide to building graph traversal queries using the Cypher syntax on Neo4j
Authored by Ravindranatha Anthapu, this practical guide focuses on using Cypher syntax to efficiently navigate and query graph databases via Neo4j. Readers are introduced to fundamental concepts and advanced techniques that make querying more manageable, thus enhancing their overall data retrieval strategy. This is essential reading for data analysts and developers who want to utilize Cypher effectively to explore their data.
10. Graph Database Modeling With neo4j : 2nd Edition
This book by Ajit Singh presents a deep dive into the modeling techniques required for successful implementation of graph databases using Neo4j. Updated with the latest techniques and strategies, it illuminates best practices that can aid both novice developers and seasoned data architects alike. Its practical orientation ensures readers can readily apply the insights in their projects, setting a strong foundational understanding of graph database modeling.
