# tGoogleCloudConfiguration properties for Apache Spark Streaming (deprecated) Availability-note[Deprecated](https://help.talend.com/r/en-US/Cloud/labels-on-talend-help-center/availability-deprecated) This content is deprecated. These properties are used to configure tGoogleCloudConfiguration running in the Spark Streaming Job framework. Information noteDeprecation: Spark Streaming is deprecated beginning with the R2026-09 release. Use Spark Structured Streaming instead. The Spark Streaming tGoogleCloudConfiguration component belongs to the Storage family. This component is available in Talend Real-Time Big Data Platform and Talend Data Fabric. ## Basic settings | Properties | Description | | --- | --- | | Project ID | Enter the ID of your Google Cloud Platform project.

If you are not certain about your project ID, confirm it in the Manage
Resources page of your Google Cloud Platform services. | | Use Google Cloud Platform credentials file | Leave this check box clear, when you
launch your Job from a given machine in which Google Cloud SDK has been
installed and authorized to use your user account credentials to access
Google Cloud Platform. In this situation, this machine is often your
local machine.

When you launch your
Job from a remote machine, such as a Talend JobServer, select this check box, then select the format of your credentials
file and in the Path to Google Credentials file
field that is displayed, enter the directory in which this credential
file is stored in the Talend JobServer machine.

In the
Service account Id field that is displayed,
enter the ID of the service account for which this P12 credentials file
has been created.

For further information about this Google
Credentials file, see the administrator of your Google Cloud Platform or
visit [Google Cloud Platform Auth
Guide](https://cloud.google.com/docs/authentication). | ## Usage | Usage guidance | Description | | --- | --- | | **Usage rule** | This component is used only when your Job needs to connect to Google
Cloud Platform while the cluster you use to run Spark is not Dataproc.

It works standalone in a subJob to provide the connection configuration
for the whole Job. | | 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. |