Neo4j Virtual Graph with Databricks
Run Cypher graph queries against your Databricks data without moving it
In this 35-minute lab, you will learn
In this lab you will learn how to use Neo4j Virtual Graph to run 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. graph queries directly against financial Full definition for transaction (opens in a new tab)A unit of work that either succeeds in full or leaves no trace. data stored in Databricks - without moving or copying that data.
You will work with a synthetic fraud ring dataset and discover how a few lines of Cypher can surface circular transaction 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. that would require tens of lines of SQL and significant query engineering to find.
Prerequisites: Familiarity with SQL and basic understanding of relational databases. No prior graph or Cypher experience required.
The Problem with SQL for Graph Patterns
Understand why recursive CTEs and self-joins struggle with circular, multi-hop transaction patterns.
How Virtual Graph Works
Learn the zero-copy architecture that lets you run Cypher directly against Databricks data.
Thinking in Graphs
Map relational tables to a graph model and read your first Cypher MATCH pattern.
Generating and Mapping the Dataset
Create the fraud ring dataset in Databricks and configure the Virtual Graph schema mapping.
Running Cypher Queries
Write and run fixed-hop ring detection queries against Databricks via Virtual Graph.