Graph Database Engineering Course: Unlocking Insights with Neo4j and Applied Data Science

Course Introduction

Graph databases are transforming the way organizations store, query, and analyze complex, interconnected data. Unlike traditional relational databases, graph databases focus on relationships, enabling businesses to uncover hidden patterns, optimize decision-making, and accelerate insights across diverse domains. This course offers a deep dive into graph database engineering with a strong focus on Neo4j, the industry-leading platform, combined with applied data science techniques.

Participants will learn how graph models differ from relational structures and why they are best suited for problems such as fraud detection, recommendation engines, supply chain optimization, and knowledge graph development. With practical sessions, learners will design, implement, and query graph databases to manage large-scale, real-world datasets.

The course emphasizes both foundational and advanced concepts, including Cypher query language, graph modeling best practices, and performance tuning for enterprise-grade workloads. By applying data science methodologies, learners will also explore graph algorithms, embeddings, and machine learning integration, unlocking powerful insights from connected data.

Emerging applications such as generative AI integration with graph databases, real-time recommendation systems, and intelligent knowledge discovery are also covered. This ensures participants are equipped with cutting-edge skills relevant for modern industries ranging from finance and telecom to healthcare and e-commerce.

Governance, scalability, and cloud deployment of graph solutions form another key area of focus. Learners will gain expertise in deploying Neo4j and other graph-based systems on-premises, in hybrid setups, and across cloud platforms like AWS, Azure, and GCP.

By the end of this program, participants will be prepared to engineer, scale, and operationalize graph database solutions that enable advanced analytics, empower intelligent applications, and unlock deep insights from highly connected data ecosystems.

Who Should Attend

• Data Engineers and Architects seeking expertise in graph modeling and engineering.
• Database Administrators transitioning from relational to graph databases.
• Data Scientists applying graph algorithms and machine learning on connected data.
• Business Intelligence and Analytics Professionals building knowledge graphs.
• Software Developers integrating Neo4j into applications and services.
• AI and ML Engineers applying graph-based machine learning techniques.
• Cloud Engineers managing graph database deployments at enterprise scale.
• Cybersecurity Analysts using graphs for fraud detection and network analysis.
• Product Managers and Consultants designing data-driven solutions with graphs.
• Researchers and Academics exploring graph theory and applied data science.

Duration

10 days

Course Objectives

• Understand the principles and architecture of graph databases.
• Build and query graph data models using Neo4j and Cypher.
• Differentiate between graph, relational, and NoSQL databases for specific use cases.
• Apply best practices in graph data modeling and optimization.
• Implement graph algorithms for clustering, centrality, and pathfinding.
• Integrate graph databases with machine learning and AI workflows.
• Explore applied use cases such as fraud detection, recommendation systems, and network analysis.
• Deploy and scale Neo4j solutions in cloud, hybrid, and enterprise environments.
• Apply security, compliance, and governance principles in graph engineering.
• Optimize performance and scalability for large graph datasets.
• Leverage emerging technologies like LLMs and generative AI in graph solutions.
• Deliver a capstone project showcasing graph engineering and applied data science integration.

Comprehensive Course Outline

Module 1: Introduction to Graph Databases

• Evolution of Databases: Relational, NoSQL, and Graph Models
• Core Concepts: Nodes, Edges, and Properties
• Graph Database Market Landscape and Trends
• Industry Applications of Graph Technologies

Module 2: Neo4j Fundamentals

• Neo4j Architecture and Components
• Installing and Configuring Neo4j
• Cypher Query Language Basics
• First Hands-On Graph Modeling

Module 3: Graph Data Modeling Principles

• Modeling Entities, Relationships, and Attributes
• Best Practices for Graph Schema Design
• Data Import and ETL for Graphs
• Handling Complex Relationships in Models

Module 4: Querying with Cypher

• Advanced Cypher Querying Techniques
• Pattern Matching and Filtering
• Aggregations, Joins, and Path Queries
• Query Performance Optimization

Module 5: Graph Algorithms Essentials

• Centrality, Community Detection, and Similarity Measures
• Pathfinding Algorithms: Shortest Path, Dijkstra, A*
• Clustering Graph Data
• Practical Applications in Fraud Detection and Social Networks

Module 6: Applied Graph Data Science

• Integrating Graphs with Data Science Workflows
• Graph Embeddings for Machine Learning
• Predictive Modeling with Graph Features
• Case Studies in Applied Graph AI

Module 7: Building Recommendation Systems

• Graph-Based Recommendation Engine Design
• Real-Time Personalization with Neo4j
• Collaborative Filtering with Graphs
• Scaling Recommendation Systems with Graph Databases

Module 8: Fraud Detection and Network Security

• Using Graphs to Detect Fraud Patterns
• Network Traffic and Threat Analysis
• Knowledge Graphs for Cybersecurity
• Real-Time Alerts and Anomaly Detection

Module 9: Knowledge Graph Engineering

• Fundamentals of Knowledge Graphs
• Ontologies and Semantic Web Integration
• Linking Graphs with Structured and Unstructured Data
• AI and LLM Integration in Knowledge Graphs

Module 10: Cloud and Enterprise Deployments

• Deploying Neo4j on AWS, Azure, and GCP
• Hybrid Architectures and Scaling Strategies
• Kubernetes and Containerized Graph Databases
• Security and Compliance in Enterprise Graphs

Module 11: Graph Integration and APIs

• Connecting Graph Databases with Applications
• REST and GraphQL APIs for Neo4j
• ETL Tools and Graph Connectors
• Real-Time Event-Driven Integrations

Module 12: Performance Tuning and Scalability

• Indexing and Caching in Graph Databases
• Query Optimization Strategies
• Scaling Large Graphs Across Clusters
• Monitoring and Observability in Graph Workloads

Module 13: Emerging Topics in Graph AI

• Generative AI with Graph Databases
• LLM-Augmented Graph Querying
• Graph Neural Networks (GNNs)
• Future Trends in Graph-Powered AI

Module 14: Governance, Security, and Compliance

• Access Controls and Authentication in Graph Databases
• GDPR, HIPAA, and Financial Compliance with Graphs
• Data Lineage and Auditing in Graph Workflows
• Ethical Considerations in Graph Data Engineering

Module 15: Advanced Use Cases and Case Studies

• Telecom Network Optimization
• Healthcare and Drug Discovery Graphs
• Financial Risk and Fraud Detection Applications
• E-Commerce Personalization at Scale

Module 16: Project – Engineering a Graph Solution

• Define a Business Problem for Graph Application
• Design and Model the Graph Schema in Neo4j
• Apply Graph Algorithms and Data Science Workflows
• Deploy and Present a Production-Ready Graph Solution

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue

The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment

Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.