Connecting Glue job to Snowflake Private Endpoint using Private Link



When you want to transform data in Snowflake for your data analytics use case, you would usually implement data transformation logic in SQL and create a view or table. On the other hand, if the logic is too complicated to implement in SQL, Snowpark, which is Snowflake version of Data Frame API, would comes in handy. Your data transformation logic in Data Frame will be transformed into SQL then your data will be processed in Snowflake.

However, only Snowpark Scala API is in generally available as of May 2022, Snowpark Python API is still in Private Preview, which is not available in production system. As an alternative solution of Snowpark Python API, AWS Glue, which is serverless Spark service in AWS, would be very useful.

In this article, I will explain how to connect Glue job with Snowflake, especially via VPC endpoint using AWS Private Link. AWS Private Link is widely used in large enterprises to connect VPCs in their own AWS accounts with 3rd party services in AWS. If you are using Private Link for your Snowflake account, your Glue job requires custom Glue connection with VPC configuration to connect with Snowflake private endpoint.

Network configuration

Assume that you have network configuration as follows.


Glue job Set up

In this section, I will explain how to set up Glue job that can connect to Snowflake private endpoint using Private Link.

Assume that you have already completed the following configurations.

  • Private Link between your own AWS VPC and Snowflake's VPC.
  • You can connect to Snowflake via Private Endpoint.

If not, please follow the instructions below.

Create a Glue connector

As mentioned previously, you need to create a Glue connector before you create a Glue connection for Snowflake JDBC. To create a Glue connector, please follow the following instruction.



  • Connector S3 URL: S3 URL you upload custom code
  • Name: Connector name
  • Connector type: JDBC
  • Class name: net.snowflake.client.jdbc.SnowflakeDriver
  • JDBC URL base: https://$account-id.$region-name.privatelink.snowflakecomputing.com:443/
    • $account-id and $region-name must be replaced with appropriate values.
  • URL parameter delimiter: &

Create a Glue connection

You can create a Glue connection in the details page of the Glue connector you previously created. Please follow this instruction to create a Glue connection.


Download Snowflake JDBC/Spark Driver

The following drivers (jar files) are required to connect to Snowflake using JDBC and load data into Data Frame.

Then upload the jar files to your S3 bucket.

Create a Glue job

You can create a new Glue job from the Glue connection details page.



  • Basic properties
  • Advanced properties
    • Script filename: (default name is the same as job name)
    • Script path: S3 URL for Glue job script
    • Spark UI logs path: S3 URL for Spark UI logs
    • Temporary path: S3 URL for working directory
  • Connections: Choose the Glue connection you created
  • Libraries
    • Dependent JARs path: S3 URLs for JDBC and dSpark drivers. You need to use , to specify more than 1 URL.s
    • e.g.s3://mybucket/snowflake-jdbc-xxx.jar,s3://mybucket/snowflake-spark-xxx.jar
  • Job parameters
    • It would be better to provide Snowflake connection parameters for better reusability. I will provide password as a parameter this time but the password should be provide as a secret in Secret Manager.
    • URL: https://$account-id.$region-name.privatelink.snowflakecomputing.com:443/
      • $account-id and $region-name must be replaced with appropriate values.
    • ACCOUNT: Snowflake account name
    • WAREHOUSE: Warehouse name
    • DB: Database name
    • SCHEMA: Schema name
    • USERNAME: User name
    • PASSWORD: Password
  • Script
import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
from py4j.java_gateway import java_import
SNOWFLAKE_SOURCE_NAME = "net.snowflake.spark.snowflake";

args = getResolvedOptions(sys.argv, ['JOB_NAME', 'URL', 'ACCOUNT', 'WAREHOUSE', 'DB', 'SCHEMA', 'USERNAME', 'PASSWORD'])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)
java_import(spark._jvm, "net.snowflake.spark.snowflake")

## uj = sc._jvm.net.snowflake.spark.snowflake
sfOptions = {
"sfURL" : args['URL'],
"sfAccount" : args['ACCOUNT'],
"sfUser" : args['USERNAME'],
"sfPassword" : args['PASSWORD'],
"sfDatabase" : args['DB'],
"sfSchema" : args['SCHEMA'],
"sfWarehouse" : args['WAREHOUSE'],

## Read from a Snowflake table into a Spark Data Frame
df = spark.read.format(SNOWFLAKE_SOURCE_NAME).options(**sfOptions).option("dbtable", "table_name").load()

## Perform any kind of transformations on your data and save as a new Data Frame: df1 = df.[Insert any filter, transformation, or other operation]
## Write the Data Frame contents back to Snowflake in a new table
df.write.format(SNOWFLAKE_SOURCE_NAME).options(**sfOptions).option("dbtable", "new_table_name").mode("overwrite").save() job.commit()

Run the Glue job

After you save the Glue job, you can run the job by pushing the Run button. If no issues with your configuration, the job will complete successfully with Succeeded status. Please check if the target table is created in the schema.


This article introduced AWS Glue job for complex data processing solution.

  • If you connect from Glue job to Snowflake private endpoint using Private Link, you need to create a Glue connection with VPC configuration.
  • Glue connection does not directly support Snowflake JDBC URL so you need to create custom Glue connection from Glue connector.

Hope this article for you to set up a Glue job for Snowflake private endpoint.