# tSnowflakeOutput properties for Apache Spark Batch (technical preview) [Preview](https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/Introduction/feature-preview-program-overview.htm) These properties are used to configure tSnowflakeOutput running in the Spark Batch Job framework. The Spark Batch tSnowflakeOutput component belongs to the Databases family. The component in this framework is available in all subscription-based Talend products with Big Data and Talend Data Fabric. This component is not supported with Spark 4.0.x. ## Basic settings | Properties | Description | | --- | --- | | Use an existing configuration | Select this check box and in the Component
List drop-down list, select the desired connection component to
reuse the connection details you already defined. | | Account | In the Account field, enter, in double
quotation marks, the account name that has been assigned to you by Snowflake. | | Snowflake Region | Select an AWS region or an Azure region from the
Snowflake Region dropdown list. | | Authentication method | Set the authentication method.

Key Pair: Select this option if key pair authentication is enabled. For more information about key pair authentication, see Using Key Pair Authentication.

Information noteNote: Before selecting the Key
Pair option, make sure you have set the key pair authentication
data in the Basic settings view of the tSetKeystore
component as follows.

Leave the TrustStore type field unchanged.
Set TrustStore file to "".
Clear the TrustStore password field.
Select Need Client authentication.
Enter the path to the key store file in double quotation marks in the KeyStore file field (or click the […] button to the right of the KeyStore file field and navigate to the key store file).
Enter the key store file password in the KeyStore password field.
Clear the Check server identity option.

Information noteNote: The Key Pair option is available only
with the EMR 5.29 and CDH 6.1 distributions when you are using Spark v2.4 and
onwards in the Local Spark mode. | | User Id and Password | Enter, in double quotation marks, your authentication information to
log in to Snowflake.

In the User ID field, enter, in double quotation marks, your login name that has been defined in Snowflake using the LOGIN_NAME parameter of Snowflake. For details, ask the administrator of your Snowflake system.
To enter the password, click the [...] button next to the password field, enter the password in double quotes in the pop-up dialog box, and click OK to save the settings.

| | Warehouse | Enter, in double quotation marks, the name of the
Snowflake warehouse to be used. This name is case-sensitive and is normally upper
case in Snowflake. | | Schema | Enter,
within double quotation marks, the name of the database schema to be used. This name
is case-sensitive and is normally upper case in Snowflake. | | Database | Enter, in double quotation
marks, the name of the Snowflake database to be used. This name is case-sensitive
and is normally upper case in Snowflake. | | Table | Click the [...] button and in the displayed wizard, select the
Snowflake table to be used. | | Schema and Edit Schema | A schema is a row description. It defines the number of fields
(columns) to be processed and passed on to the next component. When you create a Spark
Job, avoid the reserved word line when naming the
fields.

Built-In: You create and store the schema locally for this component
only.

Repository: You have already created the schema and stored it in the
Repository. You can reuse it in various projects and Job designs.

If the Snowflake data type to be handled is VARIANT, OBJECT, or ARRAY, while
defining the schema in the component, select String for the
corresponding data in the Type column of the schema editor
wizard.

Click Edit
schema to make changes to the schema. If the current schema is of the Repository type, three options are available:

View schema: choose this option to view the schema only.
Change to built-in property: choose this option to change the schema to Built-in for local changes.
Update repository connection: choose this option to change the schema stored in the repository and decide whether to propagate the changes to all the Jobs upon completion.

If you just want to propagate the changes to the current Job, you can select No upon completion and choose this schema metadata again in the Repository Content window.

Note that if the input value of any non-nullable primitive
field is null, the row of data including that field will be rejected. | | Output Action | Only the Insert action is supported
by Snowflake on Spark. | | Connection
properties | Enter, in double quotation marks, a connection property and
the associated value in the corresponding columns. You can find the properties
available in [Setting Configuration Options for the
Connector](https://docs.snowflake.com/en/user-guide/spark-connector-use.html#setting-configuration-options-for-the-connector) from the official Snowflake documentation. | ## Usage | Properties | Description | | --- | --- | | **Usage rule** | This component is used as an end component and requires an input link.

Use a tSnowflakeConfiguration:
update component in the same Job to connect to Snowflake. | | Spark Connection | In the Spark
Configuration tab in the Run
view, define the connection to a given Spark cluster for the whole Job. In
addition, since the Job expects its dependent jar files for execution, you must
specify the directory in the file system to which these jar files are
transferred so that Spark can access these files:

Yarn mode (Yarn client or Yarn cluster):
- When using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab.
- When using HDInsight, specify the blob to be used for Job deployment in the Windows Azure Storage configuration area in the Spark configuration tab.
- When using Altus, specify the S3 bucket or the Azure Data Lake Storage for Job deployment in the Spark configuration tab.
- When using on-premises distributions, use the configuration component corresponding to the file system your cluster is using. Typically, this system is HDFS and so use tHDFSConfiguration.
Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration Apache Spark Batch or tS3Configuration Apache Spark Batch.

If you are using Databricks without any configuration component present in your Job, your business data is written directly in DBFS (Databricks Filesystem).

This connection is effective on a per-Job basis. |