Graph Database Engineering with Neo4j

In an increasingly connected world, understanding relationships within data has become essential for building intelligent applications and solving complex problems. From recommendation systems and fraud detection to knowledge graphs and operational analytics, modern software relies on efficiently modeling and querying connected data. Graph databases are purpose-built for this challenge, and Neo4j stands as the leading platform for managing relationships at scale.
This book provides a practical and structured guide to working with Neo4j, combining foundational graph concepts with real-world engineering techniques. It equips readers with the knowledge needed to design, query, and analyze graph data effectively, while avoiding common pitfalls encountered in production systems.
What You'll Learn
This book offers a clear roadmap for mastering Neo4j, blending theory with hands-on application:

1. Understanding Graph Databases
   Learn why graph databases excel at modeling connected data, how they differ from traditional databases, and when Neo4j is the right choice.
2. Cypher Query Language
   Master Cypher to query, traverse, and manipulate graph data efficiently, with a focus on clarity, performance, and scalability.
3. Data Modeling and Optimization
   Discover best practices for designing flexible and efficient graph models, including indexing, constraints, and performance considerations.
4. Graph Analytics and Algorithms
   Explore Neo4j's graph analytics capabilities to uncover patterns, analyze networks, and extract meaningful insights from connected data.
5. Building Intelligent Applications
   Learn how Neo4j integrates into modern application architectures and supports graph-powered features through APIs and programming languages.
6. Real-World Use Cases
   See how graph databases are applied to practical scenarios such as fraud detection, recommendation systems, knowledge graphs, and supply chain analysis.
7. Maintenance and Troubleshooting
   Understand how to monitor performance, troubleshoot queries, and maintain reliable graph database systems.