Snowflake

Allows read and write operations on Snowflake

How to configure Key Pair Authentication on Snowflake?

Please refer the snowflake official documentation to attach private key to user account: Configuring Key Pair Authentication

Source

Source Parameters

Parameter	Description	Required
Dataset Name	Name of the Dataset	True
Credential Type	Credential Type: Databricks Secrets or Username & Password	True
Credentials	Databricks credential name , else username and password for the snowflake account	Required if Credential Type is Databricks Secrets
Username	Login name for the Snowflake user	Required if Credential Type is Username & Password
Password	Password for the Snowflake user	Required if Credential Type is Username & Password
Private key filepath	Location of Private key filepath in PKCS8 format	Required if Private Key Details is enabled
Private key passphrase	Passphrase of Private key file	Required if private key file is passphrase enabled
Url	Hostname for your account in the format: <account_identifier>.snowflakecomputing.com. Eg: https://DJ07623.ap-south-1.aws.snowflakecomputing.com	True
Database	Database to use for the session after connecting	True
Schema	Schema to use for the session after connecting	True
Warehouse	Default virtual warehouse to use for the session after connecting	False
Role	Default security role to use for the session after connecting	False
Data Source	Strategy to read data: DB Table or SQL Query.	True
Table	Name of the table to be read. All columns and records are retrieved (i.e. it is equivalent to SELECT * FROM table).	Required if Data Source is DB Table
SQL Query	Exact query (SELECT statement) to run	Required if Data Source is SQL Query

Example

Generated Code

Python

def sf_customer(spark: SparkSession) -> DataFrame:
    from pyspark.dbutils import DBUtils

    return spark.read\
        .format("snowflake")\
        .options(
          **{
            "sfUrl": "https://DJ07623.ap-south-1.aws.snowflakecomputing.com",
            "sfUser": "anshuman",
            "sfPassword": "*******",
            "sfDatabase": "SNOWFLAKE_SAMPLE_DATA",
            "sfSchema": "TPCDS_SF100TCL",
            "sfWarehouse": "COMPUTE_WH"
          }
        )\
        .option("dbtable", "CUSTOMER")\
        .load()

Scala

object customer_snow_src {
  def apply(spark: SparkSession): DataFrame = {
    import com.databricks.dbutils_v1.DBUtilsHolder.dbutils
    var reader = context.spark.read
      .format("snowflake")
      .options(
        Map(
          "sfUrl" → "https://DJ07623.ap-south-1.aws.snowflakecomputing.com",
          "sfUser" → "anshuman",
          "sfPassword" → "******",
          "sfDatabase" → "SNOWFLAKE_SAMPLE_DATA",
          "sfSchema" → "TPCDS_SF100TCL",
          "sfWarehouse" → "COMPUTE_WH"
        )
      )
    reader = reader.option("dbtable", "CUSTOMER")
    reader.load()
  }
}

---

Target

Target Parameters

Parameter	Description	Required
Dataset Name	Name of the Dataset	True
Credential Type	Credential Type: Databricks Secrets or Username & Password	True
Credentials	Databricks credential name , else username and password for the snowflake account	Required if Credential Type is Databricks Secrets
Username	Login name for the snowflake user	Required if Credential Type is Username & Password
Password	Password for the snowflake user	Required if Credential Type is Username & Password
Private key filepath	Location of Private key filepath in PKCS8 format	Required if Private Key Details is enabled
Private key passphrase	Passphrase of Private key file	Required if private key file is passphrase enabled
Url	Hostname for your account in the format: <account_identifier>.snowflakecomputing.com. Eg: https://DJ07623.ap-south-1.aws.snowflakecomputing.com	True
Database	Database to use for the session after connecting	True
Schema	Schema to use for the session after connecting	True
Warehouse	Default virtual warehouse to use for the session after connecting	False
Role	Default security role to use for the session after connecting	False
Table	Name of the table to which data is to be written.	True
Write Mode	How to handle existing data. See this table for a list of available options.	True
Post-Script SQL	DDL/DML SQL statements to execute before writing data. It is intended for statements that do not return a result set, for example DDL statements like CREATE TABLE and DML statements like INSERT, UPDATE, and DELETE. It is not useful for statements that return a result set, such as SELECT or SHOW	False

Supported Write Modes

Write Mode	Description
overwrite	If data already exists, overwrite with the contents of the DataFrame
append	If data already exists, append the contents of the DataFrame
ignore	If data already exists, do nothing with the contents of the DataFrame. This is similar to a CREATE TABLE IF NOT EXISTS in SQL.
error	If data already exists, throw an exception.

Example

Generated Code

Python

def customer_snow_tg(spark: SparkSession, in0: DataFrame):
    from pyspark.dbutils import DBUtils
    options = {
        "sfUrl": "https://DJ07623.ap-south-1.aws.snowflakecomputing.com",
        "sfUser": "anshuman",
        "sfPassword": "******",
        "sfDatabase": "SNOWFLAKE_SAMPLE_DATA",
        "sfSchema": "TPCDS_SF100TCL",
        "sfWarehouse": "COMPUTE_WH"
    }
    writer = in0.write.format("snowflake").options(**options)
    writer = writer.option("dbtable", "CUSTOMERS")
    writer = writer.mode("overwrite")
    writer.save()

Scala

object customer_snow_tg {
  def apply(spark: SparkSession, in: DataFrame): Unit = {

    import net.snowflake.spark.snowflake.Utils
    import com.databricks.dbutils_v1.DBUtilsHolder.dbutils
    val options = Map("sfUrl" → "https://DJ07623.ap-south-1.aws.snowflakecomputing.com",
                      "sfUser" → "anshuman",
                      "sfPassword" → "******",
                      "sfDatabase" → "SNOWFLAKE_SAMPLE_DATA",
                      "sfSchema" → "TPCDS_SF100TCL",
                      "sfWarehouse" → "COMPUTE_WH"
    )
    var writer = in.write.format("snowflake").options(options)
    writer = writer.option("dbtable", "test_table")
    writer = writer.mode("overwrite")
    writer.save()
  }
}