What problem does it solve? Moving large datasets between Neo4j and Spark or Databricks requires correct connector configuration, version matching, and write strategies; misconfiguration causes deadlocks, duplicate nodes, or out-of-memory failures. This Skill provides the exact Maven coordinates, option tables, and code patterns to do it correctly. ## Core Features & Use Cases - DataFrame Reads: Load Neo4j data via label scans, Cypher queries, or relationship scans with partition and batch tuning for large graphs. - DataFrame Writes: Write nodes with CREATE or MERGE (SaveMode + node.keys) and relationships with source/target key mapping, including deadlock avoidance via coalesce(1). - Databricks Integration: Cluster library installation, secrets-based credential management, Unity Catalog notes, and Delta Lake to Neo4j ingestion pipelines. - Use Case: You have a Delta Lake table of customers and orders in Databricks and need to build a Neo4j graph; this Skill gives you the exact PySpark code to MERGE customer nodes, then write ORDERED relationships with proper key mapping. ## Quick Start Ask the assistant to write PySpark code that reads a Delta table and writes it to Neo4j as Customer nodes using the Neo4j Spark connector with MERGE on customer_id.
