Neo4j Mastery Roadmap – Topics from Basics to Advanced

 1. Foundations of Graph Databases

  • What is a Graph Database?

  • Difference between Graph DB and Relational DB (RDBMS)

  • Types of Graph Databases (Property Graphs vs RDF)

  • Key Use Cases for Graph Databases (Recommendation systems, Fraud detection, Knowledge graphs, etc.)

  • Introduction to Neo4j and its ecosystem

2. Neo4j Basics

  • Installation & Setup (Desktop, AuraDB, Docker)

  • Neo4j Browser & Neo4j Bloom

  • Basic Terminology:

    • Node

    • Relationship

    • Properties

    • Labels

    • Relationship Types

  • Data Model Design Basics (How to think in graphs)

3. Cypher Query Language – The Heart of Neo4j

3.1. Basics

  • Introduction to Cypher Syntax

  • CREATE Nodes & Relationships

  • MATCH – Querying Existing Data

  • RETURN – Projecting Data

  • WHERE – Filtering Data

  • Using Aliases in Queries

3.2. Intermediate Cypher

  • MERGE vs CREATE

  • Updating Data: SET, REMOVE

  • Deleting Data: DELETE, DETACH DELETE

  • Relationship Direction & Optional Matches

  • Pattern Matching Basics (()-[]->())

  • Aggregations: COUNT, COLLECT, SUM, AVG, etc.

  • Working with Strings, Dates, and Numbers in Cypher

  • Using Parameters in Queries ($param)

3.3. Advanced Cypher

  • Variable-Length Paths (*1..3)

  • Path Functions: length(), nodes(), relationships()

  • Advanced Filtering with Pattern Comprehensions

  • Subqueries (CALL { ... })

  • List Operations in Cypher (UNWIND, WITH)

  • Working with NULLs

  • Performance Optimization using Indexes & Constraints (CREATE INDEX, CREATE CONSTRAINT)

4. Neo4j Data Modeling – From Theory to Practice

  • Translating real-world data into graph structures

  • Modeling one-to-many and many-to-many relationships

  • Designing schema for:

    • Social networks

    • Recommendation engines

    • Knowledge graphs

    • Hierarchical data

  • Avoiding Anti-Patterns (Supernodes, deeply nested relationships)

5. Indexing & Constraints

  • Schema indexes vs full-text indexes

  • Constraints (UNIQUE, NODE KEY, property existence)

  • When & where to use indexes

  • Analyzing queries with EXPLAIN and PROFILE

6. Importing & Exporting Data

  • Loading data from CSV (LOAD CSV)

  • Importing from JSON / APIs

  • Bulk imports (neo4j-admin import tool)

  • Exporting data to CSV, JSON

  • APOC procedures for ETL

7. Neo4j APOC Library (Advanced Procedures)

  • Introduction to APOC (Awesome Procedures On Cypher)

  • Data generation (apoc.create.*)

  • Graph algorithms in APOC

  • Data integration (apoc.load.json, apoc.load.csv)

  • Working with date/time & text functions

  • Exporting & visualization (apoc.export.*)

8. Graph Algorithms & Analytics

  • Overview of Graph Data Science (GDS) library

  • Centrality Measures (PageRank, Degree Centrality, Betweenness)

  • Community Detection (Louvain, Label Propagation)

  • Pathfinding Algorithms (Dijkstra, A* Search)

  • Similarity algorithms (Jaccard, Cosine similarity)

  • Link Prediction models

9. Performance & Scaling

  • Query tuning with PROFILE and EXPLAIN

  • Understanding the query planner

  • Index utilization

  • Caching strategies

  • Handling large datasets efficiently

  • Scaling Neo4j (Clustering & Causal Clusters)

10. Neo4j with Programming Languages

  • Python & Neo4j:

    • Installing neo4j Python driver

    • Running queries from Python

    • Parameterized queries

    • Building CRUD APIs with Neo4j + FastAPI

  • Neo4j with JavaScript (Node.js driver)

  • Neo4j with Java / Spring Data Neo4j

11. Integration with Other Tools

  • Connecting Neo4j to Kafka

  • Connecting Neo4j to Elasticsearch

  • Neo4j with GraphQL (Neo4j-GraphQL library)

  • Neo4j with Spark for big data pipelines

  • Integrating Neo4j with BI tools (Tableau, Power BI)

12. Security & Access Control

  • User roles & permissions

  • Fine-grained access control

  • Securing connections (SSL/TLS)

  • Authentication & Authorization best practices

13. Deploying Neo4j

  • Local deployment (Desktop, Docker)

  • Cloud deployment (Neo4j Aura, AWS, Azure, GCP)

  • Clustering & High Availability

  • Backup & Restore strategies

14. Advanced Real-World Applications

  • Recommendation engines in Neo4j

  • Fraud detection using graph patterns

  • Knowledge graph construction

  • Network analysis & visualization

  • Social network analysis

15. Expert Level – Becoming a Neo4j Power User

  • Advanced Cypher tricks & pattern comprehensions

  • Custom plugins & procedures

  • Building a custom GraphQL API layer

  • Hybrid graph + relational data models

  • Performance profiling in production

  • Handling billions of nodes/relationships


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