Zero to Production Hands-On Workshop
Go from zero to production in this hands-on Neo4j workshop. Learn Cypher basics, set up Aura, import relational data, and build an AI agent.
In this 1-hour workshop, you will learn
Zero to Production is a hands-on Neo4j workshop that takes you from your first graph query to a working AI agent in a single session. You learn how a graph database is structured, write your first Full definition for Cypher (opens in a new tab)Neo4j's implementation of GQL, the ISO standard query language for graph databases. It is declarative: you describe the pattern to find, and the database decides how to find it. queries, create your own Neo4j Go to glossary for Aura instance (opens in a new tab)A single Neo4j database running in Aura., import relational data into it, and finish by building dashboards and an AI agent that lets you converse with your data in plain language.
This course is the companion to the instructor-led workshop, containing every explanation, query, and exercise from the session - follow along live, catch up on a step you missed, or take it at your own pace afterwards. Everything you build - your Aura instance, your data, and your agent - remains yours when the workshop ends.
Basic Graph Theory
Understand nodes, relationships, and properties as the building blocks of a graph, and apply these concepts to model real-world connected data.
The Structure of a Neo4j Graph Database
Learn how Neo4j organises data as a labeled property graph, including how labels, relationship types, and properties work together.
How to Navigate a Neo4j Database
Explore the Neo4j Browser and Aura console to run queries, inspect schema, and visualise results using built-in graph exploration tools.
How to Write Cypher Queries
Write Cypher MATCH, WHERE, RETURN, and CREATE clauses to retrieve and manipulate connected data in a Neo4j graph database.
Creating a Neo4j Aura Instance
Configure and launch a free Neo4j Aura cloud instance, connect to it, and verify it is ready to ingest and query graph data.
Importing Relational Data into Neo4j
Transform and load data from a relational source into Neo4j using import tools, mapping tables and foreign keys to nodes and relationships.
Using Aura Explore, Dashboards, and Agents
Build visual dashboards, explore graph data interactively in Aura Explore, and configure an AI agent to query your graph in natural language.
What you need to take part.
A GraphAcademy account
The workshop is delivered here on GraphAcademy, so you need to be signed in to work through the lessons and keep your progress. Creating an account is free.
4 modules, 1 hour.
Who this workshop is for
This workshop is for anyone who wants a fast, practical introduction to Neo4j that ends with something running. Developers, data engineers, analysts, and technical decision-makers all take it as a first look at the platform. There are no prerequisites: the opening module teaches graph concepts from scratch, every query is explained before you run it, and the database you query at the start is provided for you. If you have worked with relational databases, the Relational to Graph material will map neatly onto what you already know, but a relational background is not required either.
What you'll do
You spend the workshop working, not watching, and it deliberately walks the same path a real project takes: understand the graph model, stand up a database in the cloud, get your data in, and put tools on top. You start by querying a live Neo4j database, matching Full definition for pattern (opens in a new tab)A graph structure written in Cypher, such as a node joined to another node by a relationship. of Full definition for node (opens in a new tab)A vertex in a graph. In a property graph it can carry labels and properties. and Full definition for relationship (opens in a new tab)A named, directed connection between two nodes. Every relationship has a type, a start node and an end node. with Full definition for Cypher (opens in a new tab)Neo4j's implementation of GQL, the ISO standard query language for graph databases. It is declarative: you describe the pattern to find, and the database decides how to find it. and creating data of your own, so the graph model stops being abstract within the first few minutes. You then move to infrastructure: after comparing the ways to run Neo4j, you create a Neo4j Go to glossary for Aura instance (opens in a new tab)A single Neo4j database running in Aura. in the cloud and take a tour of the console, so you leave with a database you own rather than a borrowed sandbox.
With your instance running, you import data. You see how tables, foreign keys, and join rows translate into nodes and relationships, then use Neo4j's import tooling to load a relational dataset into your graph. Finally, you put the graph to work: you explore it visually, build dashboards that surface metrics from your data, and create an AI agent on Go to glossary for Aura (opens in a new tab)Neo4j's fully managed cloud service. that answers natural-language questions by querying the graph, before an open-ended experiment lesson lets you push it further.
Where to go next
The natural next step is Neo4j Fundamentals, which consolidates the graph concepts from the first module, followed by Cypher Fundamentals to take your query skills further. To go deeper on what you built in the final module, Neo4j Aura Agents covers agents in detail and Neo4j Aura Dashboards does the same for dashboards, while Importing Data Fundamentals expands on the import tooling. For more live, instructor-led sessions like this one, see the workshops page.