Course · Part of Graph Data Science

Aura Graph Analytics fundamentals

Aura Graph Analytics is on-demand, session-based graph compute — pay-per-minute, isolated from your live database, and able to read graphs from any data source. Learn what AGA is, when to reach for it, and how to drive sessions from Cypher, Python, and any Neo4j driver.

1 hour11 lessons across 3 modules
About this course

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 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 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) 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 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. library.

You should already 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. (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
  • AGA sessions

  • The persistence tax

  • Cypher API in the Aura Workspace

  • Model Catalog — training, storing, and publishing

  • Python GDS client for AGA

  • Native and Cypher projections

  • Standalone mode with Pandas

  • AGA from the neo4j driver