neo4j-spark-skill

What problem does it solve?

This Skill eliminates the friction of connecting Apache Spark or Databricks to Neo4j by providing a reliable way to ingest data into DataFrames and persist updates back into the graph.

Core Features & Use Cases

• SparkSession + Connector Setup: Configure the Neo4j Connector for Apache Spark (including Databricks library install patterns) with the correct Maven artifact for your Spark/Scala runtime.
• DataFrame Reads: Load nodes by label, run Cypher for read projections, or scan relationships with source/target label constraints.
• DataFrame Writes: Write nodes and relationships using SaveMode, including MERGE overwrite semantics via node.keys, plus partition/batch tuning to reduce lock contention.
• Databricks Credentials Best Practices: Use Databricks secrets and cluster settings (including Unity Catalog shared-mode notes) to avoid hardcoding credentials.
• Delta Lake → Neo4j Pipelines: Ingest from Delta tables into Neo4j using an end-to-end Spark write pattern, including a recommended two-phase node-then-relationship workflow.

Quick Start

Use the neo4j-spark-skill to connect your Spark or Databricks job to Neo4j, read nodes into a DataFrame by label, and write them back to Neo4j with MERGE using node.keys for stable overwrites.