Intermediate

Workshop: analyze graphs in Python with the GDS client and Aura Graph Analytics. Run PageRank, Louvain, FastRP embeddings, and pathfinding at scale.

Hands-on graph data science workshop: apply Louvain, WCC, and degree centrality graph algorithms to detect fraud communities in a Neo4j sandbox.

Take your Cypher skills further in Neo4j: advanced filtering, variable-length traversal, pipelining with WITH, subqueries, UNWIND, and parameters.

Speed up Neo4j queries with Cypher constraints and indexes: uniqueness, node keys, range, composite, text, and full-text indexes, all hands-on.

Work with Neo4j temporal types in Cypher: convert dates, truncate values for grouping, calculate durations, and build indexed date range queries.

Learn Aura Graph Analytics: on-demand, session-based graph compute. Run graph algorithms from Cypher, Python, or any driver, isolated from your DB.

Create and query Neo4j full-text indexes with Lucene syntax: wildcards, fuzzy matching, boolean operators, and relevance scoring for text search.

Define and enforce a Neo4j graph schema with graph types: typed nodes and relationships, KEY and UNIQUE constraints, and incremental evolution.

Master centrality graph algorithms in Aura Graph Analytics. Sort them into six families, then run each against a real network of 59,798 messages.

Write Cypher that imports CSV data into Neo4j. Create nodes and relationships with LOAD CSV, cast data types, and batch large imports in transactions.

Learn Neo4j Graph Data Science fundamentals hands-on: project graphs, run and configure graph algorithms, and interpret results on a real movie dataset.

Extract structured communication metadata from documents and build entity networks in Neo4j

Learn how knowledge graphs, vector search, and GraphRAG ground LLMs in facts. Build RAG pipelines and retrievers in Python to stop hallucination.

Learn semantic search with Neo4j vector indexes. Create embeddings with LLMs, chunk unstructured data with LangChain, and build a knowledge graph.

Start building knowledge graphs with LLMs. Use the Neo4j LLM Graph Builder to turn unstructured text into a knowledge graph and query it with Cypher.

Give AI agents persistent memory with Neo4j context graphs. Build short-term, long-term, and reasoning memory, then query the agent's full trace.

Learn to connect AI agents to Neo4j with the Model Context Protocol. Install the Neo4j MCP server, query graphs in natural language, and build apps.

Build a custom MCP server in Python with FastMCP. Create Neo4j graph-backed tools, resources, and prompts that ground AI agents with GraphRAG.

Create a Model Context Protocol server in TypeScript. Define type-safe Zod tools, resources, and prompts that connect AI agents to Neo4j graph data.

Build agents on Neo4j Aura without code. Design an agent, add Cypher Template, Text2Cypher, and Similarity Search tools, then publish it over MCP.

Learn to connect Python applications to Neo4j with the official Python driver. Run Cypher queries, stream results into pandas, and handle errors.

Learn to connect Java applications to Neo4j using the official Java driver. Execute Cypher, map results to Java objects, and manage transactions.

Learn to connect Go applications to Neo4j with the official Neo4j Go driver. Run Cypher from Golang, handle results, transactions, and errors.

Learn to connect .NET applications to Neo4j from C# with the official .NET driver. Run Cypher queries, map results to classes, and handle errors.

Learn to use Neo4j with TypeScript: install the JavaScript driver, run Cypher queries, read and write graph data, and type-check results with generics.

Master Spring Data Neo4j: map your graph to Java domain classes, build repositories and controllers, and query Neo4j with derived methods and Cypher.

Build a Neo4j GraphQL API: write type definitions, query and mutate graph data, add custom logic with @cypher, and deploy the Neo4j GraphQL Library.

Run Neo4j Aura in production. Monitor CPU, memory, and query performance, analyze logs, and manage backup and restore for your managed graph database.

Deploy Neo4j in Docker from the command line, then scale your deployment into a fault-tolerant cluster with three primaries and two secondaries.