#Graph Algorithms (13)
- Graph Thinking: Why Connections Matter
This chapter introduces the core paradigm of graph thinking, exploring fundamental concepts, essential terminology, and diverse real-world applications to establish a foundational understanding.
- Graph Algorithms: Traversal, Pathfinding, Ranking Internals
Building on graph fundamentals, this chapter introduces essential algorithms, mastering core traversal, pathfinding, and ranking techniques, enabling practical graph problem-solving.
- Graph Data Modeling: From Business Needs to Schema
This chapter guides engineers in translating complex business requirements into robust, optimized, and scalable graph data models, leveraging fundamental concepts from prior lessons.
- Graph Data Storage: Architectures for Connected Data
This chapter details fundamental graph data representations, from adjacency matrices to property graphs, and explores the architectural paradigms of native and non-native graph databases.
- Cypher, Gremlin: Graph Query Language Paradigms
This chapter guides you through the core syntax and paradigms of Cypher and Gremlin, enabling proficient querying, manipulation, and analysis of complex graph datasets for real-world applications.
- Advanced Graph Algorithms: Community, Centrality, Similarity
Extend foundational graph algorithms by applying advanced techniques for community detection, centrality, and similarity, extracting deeper, actionable insights from complex graph data.
- Graph Scaling: Distributed Architectures for Production
This chapter explores essential strategies for scaling graph solutions, from optimizing query performance to distributing vast datasets across clusters for production-grade systems.
- Enterprise Graph Integration: Data Flow Strategies
This chapter explores essential strategies and architectural patterns for integrating graph databases into established enterprise data ecosystems, ensuring seamless data flow and application interoperability.
- Graph ML: Embeddings and GNNs for Prediction
This chapter introduces how graph structures fundamentally enhance machine learning, covering essential concepts like graph embeddings and the foundational principles of Graph Neural Networks.
- Production Graph Systems: Deployment & Operations
This chapter details best practices for deploying, monitoring, securing, and maintaining production-grade graph systems, ensuring robustness, high availability, and optimal performance.
- Graph Engineering Patterns: Successes, Failures, and Why
This chapter dissects prevalent graph architectural patterns and anti-patterns observed in production systems, guiding you to design robust, scalable, and maintainable graph solutions.
- Graph Engineering: From Fundamentals to Production Systems
This course guides engineers and data scientists from foundational graph concepts and algorithms to designing and deploying complex graph-based applications, focusing on practical, production-ready skills.
- Production Graph System: Capstone Project Build
Design, implement, and evaluate a comprehensive, scalable graph solution for a real-world problem, integrating all course principles for production readiness.