Graph Engineering: From Fundamentals to Production Systems

• Design robust graph data models for complex problems.
• Implement and optimize core and advanced graph algorithms.
• Utilize graph query languages effectively for data analysis.
• Integrate graph solutions into existing enterprise architectures.
• Deploy, monitor, and scale graph applications in production.
• Apply graph machine learning concepts for predictive tasks.

This course is meticulously designed for engineers and data scientists eager to master graph technology, guiding you from foundational principles to deploying robust, production-ready graph systems. We begin by cultivating a “graph thinking” mindset, moving beyond traditional data paradigms to understand how relationships drive insight. From there, we systematically explore the various ways graph data can be represented and efficiently stored, establishing the bedrock for all subsequent work.

Each chapter builds directly upon the knowledge acquired in the preceding ones, ensuring a cohesive and progressively sophisticated learning experience. You will first grasp essential graph algorithms before delving into the critical skill of effective graph data modeling, learning to structure data optimally for performance and clarity. With a solid model in place, we introduce powerful graph query languages like Cypher and Gremlin, enabling you to interact with and extract value from your graph data.

The journey continues into advanced graph analytics, exploring sophisticated algorithms and techniques for deeper insights, followed by practical strategies for scaling graph solutions to handle real-world data volumes. We then integrate graphs into broader enterprise architectures, discuss the exciting field of graph machine learning, and provide comprehensive guidance on deploying, monitoring, and operating graph applications in production environments. The course culminates in exploring real-world engineering patterns and a capstone project, where you will design and build a complete production graph system. Our deliberate pacing ensures every concept is absorbed, equipping you with the practical skills to confidently tackle complex graph engineering challenges without ever re-teaching or repeating content. You will emerge capable of transforming raw data into powerful, relationship-driven applications.

Chapters

1. 01 Graph Thinking: Why Connections Matter 7m
2. 02 Graph Data Storage: Architectures for Connected Data 8m
3. 03 Graph Algorithms: Traversal, Pathfinding, Ranking Internals 7m
4. 04 Graph Data Modeling: From Business Needs to Schema 8m
5. 05 Cypher, Gremlin: Graph Query Language Paradigms 9m
6. 06 Advanced Graph Algorithms: Community, Centrality, Similarity 9m
7. 07 Graph Scaling: Distributed Architectures for Production 9m
8. 08 Enterprise Graph Integration: Data Flow Strategies 9m
9. 09 Graph ML: Embeddings and GNNs for Prediction 8m
10. 10 Production Graph Systems: Deployment & Operations 8m
11. 11 Graph Engineering Patterns: Successes, Failures, and Why 8m
12. 12 Production Graph System: Capstone Project Build 9m