Neo4j

A complete Neo4j path covering graph modeling, Cypher, constraints and indexes, query planning, transactions, import, APOC, drivers, clustering, backup, security, observability, composite databases, change data capture, full-text and vector search, GraphRAG, Graph Data Science, algorithms, machine learning, performance engineering, and production architecture.

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27chapters

135lesson paths

Beginner → Advancedlearning level

Publishedcourse state

Coverage baselineCurrent Neo4j DBMS/Cypher concepts, 2026-era vector SEARCH capabilities, Graph Data Science, clustering, CDC, and modern application integration

Course brief

Think in connected structures, express graph patterns precisely, and operate Neo4j as a production database and analytical graph platform.

This syllabus deliberately separates foundations, modeling, querying, internals, reliability, security, performance, operations, and production design so advanced material is not compressed into generic catch-all chapters.

By the end

You will be able to

Model connected domains with labels, relationship types, properties, constraints, and access-pattern-aware graph structures
Write and tune Cypher using pattern matching, subqueries, indexes, execution plans, transactions, and profiling evidence
Import, integrate, secure, back up, cluster, monitor, troubleshoot, and upgrade Neo4j deployments
Build full-text, vector, hybrid GraphRAG, CDC, and application-driver workflows without confusing semantic search with graph traversal
Use Graph Data Science projections, algorithms, embeddings, and ML pipelines while controlling memory, concurrency, and production risk

Complete syllabus

27 chapters · 135 lessons.

Every lesson path was reserved in the planned syllabus and is now linked because its lesson HTML is available. The sequence moves from foundations through advanced implementation, architecture, operations, reliability, security, tuning, and a production capstone.

Chapter 1

Graph Database Foundations, Neo4j Editions, Deployment Choices, and Lab Setup

5 lessons

Graph Databases vs Relational, Document, and Search Systems: Connectivity, Traversal, and Workload Fit Published lesson · Chapter01/Lesson1.html

Neo4j

Think in connected structures, express graph patterns precisely, and operate Neo4j as a production database and analytical graph platform.

You will be able to

27 chapters · 135 lessons.

Graph Database Foundations, Neo4j Editions, Deployment Choices, and Lab Setup

Property Graph Model: Nodes, Relationships, Labels, Properties, Types, and Schema Discipline

Cypher Fundamentals: MATCH, RETURN, WHERE, Parameters, Ordering, and Pagination

Advanced Pattern Matching: Variable Length, OPTIONAL MATCH, Paths, Quantified Patterns, and Shortest Paths

Cypher Expressions, Aggregation, UNWIND, Collections, Maps, and Subqueries

Graph Writes: CREATE, MERGE, SET, REMOVE, DELETE, and Idempotent Mutation

Graph Data Modeling: Aggregates, Relationships, Hyperedges, Hierarchies, and Temporal Graphs

Importing Data: LOAD CSV, Data Importer, Bulk Import, Transformation, and Validation

Constraints and Indexes: Range, Text, Point, Token, Full-Text, and Schema Enforcement

Query Planning and Profiling: Cardinality, Operators, Index Selection, and Plan Stability

Transactions, Isolation, Locking, Deadlocks, Retries, and Consistency

APOC, Procedures, Functions, Triggers-Like Workflows, and Extending Cypher

Application Drivers and Bolt: Sessions, Routing, Parameters, Result Streaming, and Connection Pools

Asynchronous, Reactive, Batch, and High-Throughput Application Patterns

Security: Authentication, RBAC, Privileges, TLS, Secrets, and Least Privilege

Backup, Restore, Transaction Logs, Disaster Recovery, and Upgrade Safety

Clustering, High Availability, Routing, Consensus, and Failure Domains

Monitoring and Troubleshooting: Metrics, Logs, Query Visibility, Memory, Store, and Capacity

Composite Databases, Multiple Databases, Federation, and Data-Domain Boundaries

Change Data Capture, Event Integration, Kafka/Connect Patterns, and Near-Real-Time Graph Synchronization

Full-Text Search, Text Analysis, Relevance, and Hybrid Retrieval with Graph Context

Vector Search, Embeddings, Cypher SEARCH, Hybrid Search, and GraphRAG

Graph Data Science Foundations: Projections, Graph Catalog, Memory, and Execution Modes

Graph Algorithms: Centrality, Community Detection, Similarity, Paths, and Topology Analytics

Graph Embeddings and Machine Learning Pipelines with GDS

Performance Engineering: Data Model, Traversal Shape, Page Cache, Memory, and Workload Isolation

Production Capstone: Model, Import, Query, Search, Analyze, Secure, Fail Over, and Operate Neo4j