# tBigQueryConfiguration properties for Apache Spark Batch

These properties are used to configure tBigQueryConfiguration running in the Spark Batch Job framework.

The Spark Batch tBigQueryConfiguration component belongs to the Storage and the Databases families.

The component in this framework is available in all subscription-based Talend products with Big Data and Talend Data Fabric.

## Basic settings

When you use this component with Google Dataproc:

| BigQuery temp GCS path | Enter the directory on Google Storage to temporarily<br>store the data to be used with Google BigQuery. If not existing yet,<br>this directory is created on the fly but the bucket that contains this<br>directory must already exist. The syntax of the directory should be<br>gs://my\_bucket/my\_directory.<br><br>When you use Google BigQuery with Dataproc, in<br>Google Cloud Platform, select the same region for your BigQuery dataset<br>as for the Dataproc cluster to be run. |
| --- | --- |
| Location | Select one of the Google multi-regional locations in which you want to<br>read or write data. The resource dataset and the target dataset must be in<br>the same location.<br><br>In Spark Jobs, only the US and the EU locations are supported.<br><br>For further information about Google locations and how<br>to properly use the locations, see [Dataset Locations](https://cloud.google.com/bigquery/docs/dataset-locations) from Google<br>documentation. |

When you use this component with the other distributions

| Project identifier | Enter the ID of your Google Cloud Platform project.<br><br>If you are not certain about your project ID, check it in the Manage<br>Resources page of your Google Cloud Platform services. |
| --- | --- |
| Path to Google Credentials<br>file | Enter the path to the credentials file associated to the<br>user account to be used. This file must be stored in the machine in<br>which your Talend<br>Job is actually launched and executed.<br><br>If you use Talend Jobserver to run your Job, store the credentials file not only in the machine of the 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 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<br>format | When the Google credentials file to be used is in P12<br>format, select this check box and then in the Service account Id field that is displayed, enter the<br>ID of the service account for which this P12 credentials file has been<br>created. |
| BigQuery temp GCS path | Enter the directory on Google Storage to temporarily<br>store the data to be used with Google BigQuery. If not existing yet,<br>this directory is created on the fly but the bucket that contains this<br>directory must already exist. The syntax of the directory should be<br>gs://my\_bucket/my\_directory.<br><br>When you use Google BigQuery with Dataproc, in<br>Google Cloud Platform, select the same region for your BigQuery dataset<br>as for the Dataproc cluster to be run. |
| Location | Select one of the Google multi-regional locations in which you want to<br>read or write data. The resource dataset and the target dataset must be in<br>the same location.<br><br>In Spark Jobs, only the US and the EU locations are supported.<br><br>For further information about Google locations and how<br>to properly use the locations, see [Dataset Locations](https://cloud.google.com/bigquery/docs/dataset-locations) from Google<br>documentation. |

## Usage

| **Usage rule** | This component is used standalone in a subJob to provide<br>connection configuration to Google BigQuery and Google Storage for the<br>whole Job. |
| --- | --- |
| Spark Connection | In the Spark<br>Configuration tab in the Run<br>view, define the connection to a given Spark cluster for the whole Job. In<br>addition, since the Job expects its dependent jar files for execution, you must<br>specify the directory in the file system to which these jar files are<br>transferred so that Spark can access these files:<br><br><ul> <li> <div> <span>Yarn mode</span> (Yarn client or Yarn cluster):<ul> <li> <p>When using Google Dataproc, specify a bucket in the                                            <span>Google Storage staging bucket</span>                                        field in the <span>Spark configuration</span>                                        tab.</p> </li> <li> <p>When using HDInsight, specify the blob to be used for Job                                        deployment in the <span>Windows Azure Storage                                            configuration</span> area in the <span>Spark                                            configuration</span> tab.</p> </li> <li>When using Altus, specify the S3 bucket or the Azure                                    Data Lake Storage for Job deployment in the <span>Spark                                        configuration</span> tab.</li> <li>When using Qubole, add a                                        <span>tS3Configuration</span> to your Job to write                                    your actual business data in the S3 system with Qubole. Without                                        <span>tS3Configuration</span>, this business data is                                    written in the Qubole HDFS system and destroyed once you shut                                    down your cluster.</li> <li> <p>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 <span></span>.</p> </li> </ul> </div> </li> <li> <p> <span>Standalone mode</span>: use the                            configuration component corresponding to the file system your cluster is                            using, such as <span>tHDFSConfiguration</span> or <span>tS3Configuration</span>.</p> <p>If you are using Databricks without any configuration component present                            in your Job, your business data is written directly in DBFS (Databricks                            Filesystem).</p> </li> </ul><br><br>This connection is effective on a per-Job basis. |