Using Microsoft Fabric Workload for AuraDB to Make Product Recommendations from OneLake Data
summary
Demonstration shows Neo4j Graph Intelligence as a Fabric workload that transforms OneLake tables (Northwind dataset) into a graph, runs a Spark import that spins up an AuraDB instance and takes a few minutes to complete, and produces product recommendations via graph queries and algorithms inside the Fabric console. Workflow uses AI-assisted graph modeling to propose nodes and relationships, edits map order items to products, runs the Neo4j Spark connector to write to AuraDB, and lets analysts explore, run Cypher queries, and write results back to OneLake; fully managed and billed only while running.
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chapters (9)
- 0:00Introduction to session
- 0:26What is Microsoft Fabric
- 1:51Graph intelligence overview
- 3:37Graph creation process
- 7:52Demo workspace setup
- 9:18Importing lakehouse data
- 10:27Editing graph model
- 12:12Exploring the graph
- 13:16How to get started
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