Aura Graph Analytics fundamentals
Learn Aura Graph Analytics: on-demand, session-based graph compute. Run graph algorithms from Cypher, Python, or any driver, isolated from your DB.
In this 1-hour course, you will learn
Go to glossary for Aura Graph Analytics (opens in a new tab)The Aura service that runs graph algorithms in a separate session, with no plugin to install. (AGA) is Neo4j's on-demand, session-based graph compute service. This course teaches you what AGA is, when to reach for it, and how to drive analytics sessions from 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. in the Go to glossary for Aura (opens in a new tab)Neo4j's fully managed cloud service. Workspace, from the Python client, and from any Neo4j driver.
With AGA, you create a session when you need it, project a graph into it from any source, run graph algorithms, optionally write results back, and tear the session down, paying only for the minutes it ran. Your graph analytics never share resources with your production database, and your graph data does not have to live in Neo4j at all. That changes the cost shape of Go to glossary for graph data science (opens in a new tab)Analysing data through the structure of its connections. Also the name of the Neo4j library that implements it.: instead of provisioning a permanently sized instance for occasional analytical workloads, you rent exactly the compute each job needs.
AGA Sessions
Understand how Aura Graph Analytics creates isolated, on-demand compute sessions that run graph algorithms without affecting your live database.
The Persistence Tax
Learn why persisting graph projections to disk carries a performance cost and how session-based compute avoids it for faster, leaner analytics workloads.
Cypher API in the Aura Workspace
Write and execute graph algorithm calls directly from the Aura Workspace using the Cypher API, without leaving your existing query environment.
Model Catalog — Training, Storing, and Publishing
Train graph machine learning models, store them in the Model Catalog, and publish them so downstream applications can consume predictions.
Python GDS Client for AGA
Configure the Python GDS client to connect to an AGA session and run graph algorithms programmatically from a Python script or notebook.
Native and Cypher Projections
Build in-memory graph projections using both native projections for performance and Cypher projections for flexible, query-driven subgraph selection.
Standalone Mode with Pandas
Load graph data from a Pandas DataFrame into an AGA session in standalone mode, enabling graph analytics on data that lives outside Neo4j.
AGA from the Neo4j Driver
Connect to an AGA session using the standard Neo4j driver and run graph algorithm procedures from any language supported by the driver ecosystem.
3 modules, 1 hour.
Who this course is for
This course is for data scientists and analysts who already know the Go to glossary for graph data science (opens in a new tab)Analysing data through the structure of its connections. Also the name of the Neo4j library that implements it. (GDS) library and want to run its algorithms as a managed cloud service. It is equally useful for engineers who operate Go to glossary for AuraDB (opens in a new tab)The Aura product for transactional workloads. instances and need to add graph analytics without resizing production, and for Python practitioners whose data lives in DataFrames rather than in a Neo4j database. You should be comfortable with Full definition for projection (opens in a new tab)An in-memory copy of part of your database that graph algorithms run against. You choose which nodes and relationships it holds., the project-run-write workflow, and the GDS execution modes before you start; the prerequisite course is listed beside this overview.
What you'll do
This is a hands-on course. You will enable Go to glossary for Aura Graph Analytics (opens in a new tab)The Aura service that runs graph algorithms in a separate session, with no plugin to install. on an Go to glossary for Aura (opens in a new tab)Neo4j's fully managed cloud service. project, attach it to an Go to glossary for AuraDB (opens in a new tab)The Aura product for transactional workloads. instance, and load a movies dataset to analyze. From there, every lesson has you running real sessions: projecting graphs, executing algorithms, inspecting costs, and cleaning up so you are not paying for compute you have finished with.
You will work in two environments. In the Aura Workspace you drive sessions directly from the Query tool using 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., covering the full session workflow, implicit sessions, catalog management, and training and publishing machine learning models. In Python you use the Go to glossary for graph data science (opens in a new tab)Analysing data through the structure of its connections. Also the name of the Neo4j library that implements it. client to manage sessions programmatically, including workloads that Cypher cannot reach, such as projecting a graph straight from Pandas DataFrames. You will also see that sessions expose a Cypher endpoint, so any application holding a
neo4j+s://connection can run analytics without the GDS client. Checks along the way confirm your sessions, Full definition for projection (opens in a new tab)An in-memory copy of part of your database that graph algorithms run against. You choose which nodes and relationships it holds., and results are correct before you move on.Where to go next
The natural next step is Understand centrality algorithms, a hands-on lab where you use an Full definition for Aura Graph Analytics session (opens in a new tab)A managed, temporary compute environment that holds a projection and runs algorithms against it. to run Full definition for centrality (opens in a new tab)How important a node is within a graph. Each centrality algorithm defines importance differently. algorithms from six different families against a real messaging network. For a deeper grounding in the algorithms themselves, Path Finding with GDS covers weighted and unweighted shortest path algorithms, all of which you can now run inside an AGA session. When you are ready to validate your skills, the Neo4j Graph Data Science certification covers Full definition for projection (opens in a new tab)An in-memory copy of part of your database that graph algorithms run against. You choose which nodes and relationships it holds., algorithm configuration, and result interpretation, the same fundamentals you have applied throughout this course.