This deep dive shows how to pair Azure Databricks with Neo4j to surface relationship intelligence for fraud, identity resolution, churn, and supply chain risk. I will design a graph-augmented data model, sync Delta <--> Neo4j reliably, run Cypher patterns (k-hop paths, triangles, community detection) and push graph features back to Delta for ML and BI. You’ll learn governance and lineage across both systems, performance/cost trade-offs and a pragmatic rollout plan. Expect practical code, reference patterns, and a compact live demo you can reuse on Azure today.