July 30, 2023

tSnowflakeOutput properties for Apache Spark Batch (technical preview) – Docs for ESB 7.x

tSnowflakeOutput properties for Apache Spark Batch (technical preview)

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.

Basic settings

Use an existing configuration

Select this check box and in the Component List click the relevant 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 drop-down list.

User Id and Password

Enter, in double quotation marks, your authentication
information to log in 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, and then in the pop-up dialog box enter the password between double quotes
    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.

Usage

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 Qubole, add a
      tS3Configuration to your Job to write
      your actual business data in the S3 system with Qubole. Without
      tS3Configuration, this business data is
      written in the Qubole HDFS system and destroyed once you shut
      down your cluster.
    • When using on-premise
      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 or
    tS3Configuration.

    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.


Document get from Talend https://help.talend.com
Thank you for watching.
Subscribe
Notify of
guest
0 Comments
Inline Feedbacks
View all comments
0
Would love your thoughts, please comment.x
()
x