Read and write Neo4j data with Apache Spark DataFrames.
npx skills add https://github.com/neo4j-contrib/neo4j-skills --skill neo4j-spark-skill
Please help me install this Agent Skill. Skill: neo4j-spark-skill Source: https://github.com/neo4j-contrib/neo4j-skills/tree/main/neo4j-spark-skill Command: npx skills add https://github.com/neo4j-contrib/neo4j-skills --skill neo4j-spark-skill
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.
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.
High-intent search queries and answers about installing and using this skill.
To read and write Neo4j data with Apache Spark DataFrames, use the Neo4j Connector for Apache Spark to load nodes via label scans, execute custom Cypher queries, and persist relationships back to the graph using SaveMode.
Yes, you can ingest Delta Lake tables into Neo4j using Databricks by reading Delta tables into Spark DataFrames and writing them to Neo4j using a recommended two-phase node-then-relationship workflow.
The best way to overwrite Neo4j nodes with Spark without duplicating data is to use SaveMode Overwrite with node.keys, which applies MERGE semantics to update existing graph entities instead of appending duplicates.
Yes, the Neo4j Connector for Apache Spark supports Databricks Unity Catalog shared-mode, and you should use Databricks secrets for cluster configuration to avoid hardcoding credentials when setting up the connection.
Writing Spark DataFrames to Neo4j causes lock contention when partition and batch sizes are not properly tuned, so you must adjust these performance parameters to reduce concurrent write conflicts during graph persistence.
Yes, you need to select the correct Maven artifact for the Neo4j Connector for Apache Spark that matches your specific Spark, Scala, and Databricks runtime version to ensure proper DataFrame ingestion and graph connectivity.
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