Aura Graph Analytics fundamentals

Aura Graph Analytics (AGA) is on-demand, session-based graph compute. You create a session when you need it, project a graph into it from any source, run algorithms, optionally write results back, and tear it down — paying only for the minutes the session ran. Your analytics never share resources with your production database, and your graph data doesn't have to live in Neo4j at all.

This course walks through what AGA is, when to reach for it, and how to drive sessions from both Cypher (in the Aura Workspace) and the Python client. Three modules:

• Understanding AGA: what it is, what you can do with it, how to switch it on.
• Aura Graph Analytics in the Aura Workspace: driving sessions directly from the Workspace's Query tool.
• From the Python client: driving it programmatically, including the workflows Cypher can't reach.

Prerequisites

This course assumes you've completed Get started with Graph Data Science or have equivalent experience with the Graph Data Science library.

You should already be comfortable with:

• Graph projections (native and Cypher)
• The Project -> Run -> Write workflow
• The execution modes (stats, stream, mutate, write, estimate)
• Reading and configuring algorithm calls

What you'll learn

• What AGA is and how it changes the cost shape of graph analytics
• The three workloads it's designed for, and when not to reach for it
• How to enable AGA on a project and create the credentials a session needs
• How to drive a session from Cypher in the Aura Workspace
• How to drive a session from the Python client, including standalone workloads on data that isn't in Neo4j