Neo4j: Fundamentals of Graph Database Technology and Connected Data Modeling | Uplatz

Traditional databases struggle when data becomes highly connected, relationship-heavy, and dynamic. Graph databases solve this challenge by treating relationships as first-class citizens—and Neo4j is the most widely adopted platform built specifically for this purpose. This video provides a clear, foundational guide to Neo4j, focusing on the core principles of graph database technology and connected data modeling.

This Uplatz Explainer introduces the fundamental building blocks of graph databases, including nodes, relationships, properties, labels, and graph traversals. You’ll learn how graph data models differ from relational and NoSQL models, and why graph databases excel at representing networks, hierarchies, dependencies, and real-world connections.

The video dives into Neo4j’s internal architecture and query model, explaining how Neo4j stores graph data, executes relationship traversals, and uses the Cypher query language to express complex queries in an intuitive, readable way. We explain why graph queries are often faster and more expressive for connected data problems than traditional joins.

A key focus of the session is connected data modeling. You’ll gain insight into how to design effective graph schemas, model relationships correctly, and avoid common anti-patterns. We also explore real-world use cases such as recommendation systems, fraud detection, identity graphs, knowledge graphs, and network analysis—highlighting where Neo4j provides a clear architectural advantage.

This video is ideal for backend developers, data engineers, architects, data scientists, and technology decision-makers who want a strong conceptual foundation in graph databases, understand when to use Neo4j, and design systems that fully leverage relationship-driven data.

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