Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

In highly connected domains such as fraud rings, supply-chain dependencies, recommendation engines and enterprise Knowledge Graphs, multi-hop relational JOINs degrade exponentially as traversal depth grows. Using index-free adjacency to store relationships as first-class pointers, Neo4j traverses deep connection paths in milliseconds.

This course covers Labeled Property Graph modelling (Nodes, Relationships, Labels, Properties), the Cypher query language and pattern matching, shortest-path and variable-length traversals, constraints/indexes and tuning with PROFILE/EXPLAIN, APOC and Graph Data Science algorithms (centrality, community detection), and vector index/GraphRAG integration.

Who Should Attend?

  • Backend engineers building fraud detection, network topology, hierarchy or recommendation systems
  • Data scientists and AI engineers building enterprise Knowledge Graphs and GraphRAG pipelines
  • Data architects modelling complex lineage, master data and impact-analysis domains
  • Prerequisite: foundational database and querying (SQL or NoSQL) knowledge

Key Takeaways

  • Translate relational schemas into query-optimised Labeled Property Graph models
  • Write expressive multi-hop queries in Cypher using MATCH, MERGE, WITH, UNWIND and shortestPath
  • Avoid supernode bottlenecks through relationship fan-out and intermediate-node patterns
  • Eliminate Cartesian products and reduce DB hits using PROFILE and EXPLAIN plans
  • Apply Neo4j Graph Data Science algorithms and hybrid vector + graph retrieval (GraphRAG)

Curriculum

Graph Fundamentals, Index-Free Adjacency and Property Graphs
  • Why Graph Databases? Multi-hop relational JOIN costs vs Index-Free Adjacency
  • Labeled Property Graph building blocks: Nodes, Labels, directed/typed Relationships and Properties
  • Translating ER diagrams into graph models and deciding between properties and separate nodes
Pattern Matching and Data Manipulation with Cypher
  • ASCII-art pattern syntax: MATCH, OPTIONAL MATCH, WHERE and RETURN
  • Idempotent writes with MERGE (ON CREATE / ON MATCH) vs CREATE, SET and DETACH DELETE
  • Pipelining stages with WITH, UNWIND, COLLECT, list comprehensions and batched ingestion
Deep Traversals, Graph Modelling Patterns and Query Tuning
  • Variable-length paths (-[:KNOWS*1..5]->), shortestPath, allShortestPaths and Quantified Path Patterns
  • Avoiding graph anti-patterns: mitigating Supernodes, fan-out relationships and intermediate event nodes
  • Range, Text and Point indexes, uniqueness constraints and tuning db hits with EXPLAIN / PROFILE
Graph Data Science (GDS) and Knowledge Graphs (GraphRAG)
  • In-memory graph projections and Centrality algorithms (PageRank, Betweenness, Degree)
  • Community detection (Louvain, WCC) and similarity/link-prediction pipelines
  • Combining Neo4j Vector Indexes with graph neighbourhood traversal for GraphRAG applications
Application Integration and Production Operations
  • The Bolt protocol, official Neo4j drivers (Java, .NET, Python, JS) and managed transaction functions
  • Backups, RBAC security, clustering/AuraDB essentials and a hands-on fraud/recommendation modelling lab

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