# tBigQueryConfiguration 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 tBigQueryConfiguration 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 tBigQueryConfiguration component belongs to the Storage and the Databases families. This component is available in Talend Real-Time Big Data Platform and Talend Data Fabric. ## Basic settings When you use this component with Google Dataproc: | Properties | Description | | --- | --- | | BigQuery temp GCS path | Enter the
directory on Google Storage to temporarily store the data to be used
with Google BigQuery. If not existing yet, this directory is created on
the fly but the bucket that contains this directory must already exist.
The syntax of the directory should be gs://my\_bucket/my\_directory.

When
you use Google BigQuery with Dataproc, in Google Cloud Platform, select
the same region for your BigQuery dataset as for the Dataproc cluster to
be
run. | | Location | Select one of the Google
multi-regional locations in which you want to read or write data. The
resource dataset and the target dataset must be in the same
location.

In Spark Jobs, only the US and the EU
locations are supported.

For further information about Google locations and how to properly use the
locations, see [Dataset Locations](https://cloud.google.com/bigquery/docs/dataset-locations) in the Google Cloud
documentation. | When you use this component with the other distributions | 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. | | Path to Google Credentials file | Enter the path to the credentials file associated to the
user account to be used. This file must be stored in the machine in
which your Talend
Job is actually launched and executed.

If you use Talend JobServer to run your Job, store the credentials file not only in the machine of the
Talend JobServer, in which the Job is launched, but also in the worker machines of the Spark
cluster, in which the Job is executed; if you do not use the Talend JobServer, store the credentials file in your local machine from which you launch the
Job and in the worker machines of the Spark cluster. | | Use P12 credentials file format | When the Google credentials file to be used is in P12
format, select this check box and then 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. | | BigQuery temp GCS path | Enter the
directory on Google Storage to temporarily store the data to be used
with Google BigQuery. If not existing yet, this directory is created on
the fly but the bucket that contains this directory must already exist.
The syntax of the directory should be gs://my\_bucket/my\_directory.

When
you use Google BigQuery with Dataproc, in Google Cloud Platform, select
the same region for your BigQuery dataset as for the Dataproc cluster to
be
run. | | Location | Select one of the Google
multi-regional locations in which you want to read or write data. The
resource dataset and the target dataset must be in the same
location.

In Spark Jobs, only the US and the EU
locations are supported.

For further information about Google locations and how to properly use the
locations, see [Dataset Locations](https://cloud.google.com/bigquery/docs/dataset-locations) in the Google Cloud
documentation. | ## Usage | Usage guidance | Description | | --- | --- | | **Usage rule** | This component is used standalone in a subJob to provide
connection configuration to Google BigQuery and Google Storage 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. |