Integrating Data from Microsoft Fabric's Lakehouse with Neo4j Graph Database

The solution provided did not help t-wth the problem. 
It started to create nodes with this code.

%%configure -f
{
  "conf": {
    "spark.jars": "abfss://[email protected]/Northwind_Lakehouse.Lakehouse/Files/Northwind/neo4j-spark-connector-5.3.1-s_2.12.jar"
  }
}

import org.apache.spark.sql.{SparkSession, SaveMode}
import org.apache.spark.sql.functions._
import com.fasterxml.jackson.databind.ObjectMapper
import com.fasterxml.jackson.module.scala.{DefaultScalaModule, ScalaObjectMapper}
import com.fasterxml.jackson.module.scala.experimental.ScalaObjectMapper


// Set base path
val absfss_Base_Path = "abfss://[email protected]/Northwind_Lakehouse.Lakehouse/Files/Northwind/"

// Read Northwind data files
val customerDF = spark.read.option("header", true).csv(s"$absfss_Base_Path/customers.csv")
val supplierDF = spark.read.option("header", true).csv(s"$absfss_Base_Path/suppliers.csv")
val stagedOrderDF = spark.read.option("header", true).csv(s"$absfss_Base_Path/orders.csv")
  .withColumn("addressID", concat_ws(", ", col("shipName"), col("shipAddress"), col("shipCity"), col("shipRegion"), col("shipPostalCode"), col("shipCountry")))
val orderDetailDF = spark.read.option("header", true).csv(s"$absfss_Base_Path/order-details.csv")
val productDF = spark.read.option("header", true).csv(s"$absfss_Base_Path/products.csv")
val categoryDF = spark.read.option("header", true).csv(s"$absfss_Base_Path/categories.csv")

// Create separate addressesDF and finalize orderDF
val addressDF = stagedOrderDF
  .select($"addressID", $"shipName".alias("name"), $"shipAddress".alias("address"), $"shipCity".alias("city"), $"shipRegion".alias("region"), $"shipPostalCode".alias("postalCode"), $"shipCountry".alias("country"))
  .dropDuplicates("addressID")

val orderDF = stagedOrderDF.drop("shipName", "shipAddress", "shipCity", "shipRegion", "shipPostalCode", "shipCountry")

// Load JSON file for Neo4j credentials
val jsonString = spark.read.text(s"$absfss_Base_Path/neo4j-conn.json").as[String].collect().mkString("\n")

// Parse JSON string
val mapper = new ObjectMapper() with ScalaObjectMapper
mapper.registerModule(DefaultScalaModule)
val data = mapper.readValue[Map[String, Any]](jsonString)

// Extract Neo4j connection details
val neo4jUrl = data("NEO4J_URL").asInstanceOf[String]
val neo4jUsername = data("NEO4J_USERNAME").asInstanceOf[String]
val neo4jPassword = data("NEO4J_PASSWORD").asInstanceOf[String]

val neo4jOptions = Map(
  "url" -> neo4jUrl,
  "authentication.basic.username" -> neo4jUsername,
  "authentication.basic.password" -> neo4jPassword
)

customerDF.write
  .format("org.neo4j.spark.DataSource")
  .mode(SaveMode.Overwrite)
  .options(neo4jOptions)
  .option("labels", "Customer")
  .option("node.keys", "customerID")
  .save()

supplierDF.write
  .format("org.neo4j.spark.DataSource")
  .mode(SaveMode.Overwrite)
  .options(neo4jOptions)
  .option("labels", "Supplier")
  .option("node.keys", "supplierID")
  .save()

stagedOrderDF.write
  .format("org.neo4j.spark.DataSource")
  .mode(SaveMode.Overwrite)
  .options(neo4jOptions)
  .option("labels", "Order")
  .option("node.keys", "orderID")
  .save()

 Yet i still do not know how to run this part succesfully

// Write nodes to Neo4j
def writeNodesToNeo4j(dataFrame: DataFrame, label: String, nodeKey: String): Unit = {
  dataFrame.write.format("org.neo4j.spark")
    .mode(SaveMode.Overwrite)
    .option("url", neo4jUrl)
    .option("authentication.basic.username", neo4jUsername)
    .option("authentication.basic.password", neo4jPassword)
    .option("labels", label)
    .option("node.keys", nodeKey)
    .option("schema.optimization.node.keys", "KEY") //create node key constraints under the hood
    .save()
}

writeNodesToNeo4j(customerDF, "Customer", "customerID")
writeNodesToNeo4j(supplierDF, "Supplier", "supplierID")
writeNodesToNeo4j(orderDF, "Order", "orderID")
writeNodesToNeo4j(productDF, "Product", "productID")
writeNodesToNeo4j(categoryDF, "Category", "categoryID")
writeNodesToNeo4j(addressDF, "Address", "addressID")