# tRedshiftConfiguration properties for Apache Spark Batch These properties are used to configure tRedshiftConfiguration running in the Spark Batch Job framework. The Spark Batch tRedshiftConfiguration component belongs to the Storage and the Databases families. The component in this framework is available in all Talend products with Big Data and Talend Data Fabric. ## Basic settings | Property type | Either Built-In or Repository.


Built-In: No property data stored centrally.


Repository: Select the repository file where the
properties are stored. | | --- | --- | | Host | Enter the endpoint of the database you need to connect to in
Redshift. | | Port | Enter the port number of the database you need to connect to in
Redshift.

The related information can be found in the Cluster Database
Properties area in the Web console of your Redshift.

For further information, see [Managing clusters console](http://docs.aws.amazon.com/redshift/latest/mgmt/managing-clusters-console.html). | | Username and Password | Enter the authentication information to the Redshift database you
need to connect to.

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. | | Database | Enter the name of the database you need to connect to in
Redshift.

The related information can be found in the Cluster Database
Properties area in the Web console of your Redshift.

For further information, see [Managing clusters console](http://docs.aws.amazon.com/redshift/latest/mgmt/managing-clusters-console.html).

The bucket and the Redshift database to be used
must be in the same region on Amazon. This could avoid the S3ServiceException errors known to
Amazon. For further information about these errors, see [S3ServiceException Errors](https://docs.aws.amazon.com/redshift/latest/dg/s3serviceexception-error.html). | | Schema | Enter the name of the database schema to be used in Redshift. The
default schema is called *PUBLIC*.

A schema in terms of Redshift is similar to a operating system
directory. For further information about a Redshift schema, see [Schemas](http://docs.aws.amazon.com/redshift/latest/dg/r_Schemas_and_tables.html). | | Additional JDBC Parameters | Specify additional JDBC properties for the connection you are creating. The
properties are separated by ampersand & and each property is a key-value pair. For
example, *ssl=true &
sslfactory=com.amazon.redshift.ssl.NonValidatingFactory*, which means the
connection will be created using SSL. | | S3 configuration | Select the tS3Configuration component from which you want Spark to use the
configuration details to connect to S3.

You need drop
the tS3Configuration component
to be used alongside tRedshiftConfiguration in the same Job so that this tS3Configuration is displayed on the
S3 configuration
list. | | S3 temp path | Enter the
location in S3 in which the data to be transferred from or to Redshift is
temporarily stored.

This path is independent of
the temporary path you need to set in the Basic settings tab of tS3Configuration. | ## Usage | **Usage rule** | This component is used with no need to be connected to other
components.

You need to drop tRedshiftConfiguration alongside the other Redshift related Subjobs to be run
in the same Job so that the configuration is used by the whole Job at runtime.

Since Redshift uses S3 to store temporary
data, you need to drop a tS3Configuration component alongside tRedshiftConfiguration in the same Job so that the
S3 configuration is used by the whole Job at runtime.

This component, along with the Spark Batch component Palette it belongs to,
appears only when you are creating a Spark Batch Job.

Note that in this documentation, unless otherwise explicitly stated, a
scenario presents only Standard Jobs, that is to
say traditional
Talend
data integration Jobs. | | --- | --- | | 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:



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