Databricks Connect

With Databricks Connect for Python and Scala, partners must set the userAgent as part of the connection parameters using the session builder.

Scala

import com.databricks.connect.DatabricksSession
import org.apache.spark.sql.SparkSession

object ScalaDbConnectTest {
  def main(args: Array[String]): Unit = {

    val spark: SparkSession =
      DatabricksSession.builder
        .userAgent("<isv-name_product-name>/<product-version>")
        .getOrCreate()

    println(s"Spark Version: ${spark.version}")

    spark.sql("SELECT current_user(), current_timestamp()").show(false)

    spark.stop()
  }
}

Python

from databricks.connect import DatabricksSession
from databricks.sdk.core import Config

def main():
    # Build SDK config from environment
    config = Config()

    user_agent = "<isv-name_product-name>/<product-version>"
    print("User-Agent:", user_agent)

    # Create Spark session with Databricks Connect
    spark = (
        DatabricksSession.builder
            .sdkConfig(config)
            .userAgent(user_agent)
            .getOrCreate()
    )

    print(f"Spark version: {spark.version}")

    # Verify current user/timestamp
    verify_df = spark.sql("SELECT current_user() AS user, current_timestamp() AS ts")
    verify_df.show(truncate=False)

    # DataFrame test
    df = spark.range(5)
    print("Result:", df.collect())

    print("Done.")

if __name__ == "__main__":
    main()

What's next

• Configure libraries: Implement telemetry for Python and Scala libraries. See Libraries & UDFs.
• Configure Lakebase: Set up telemetry for PostgreSQL-compatible clients. See Lakebase Integrations.
• Review User-Agent format: Understand the required format and guidelines. See Telemetry & Attribution.