# tBigQueryInput properties for Apache Spark Batch These properties are used to configure tBigQueryInput running in the Spark Batch Job framework. The Spark Batch tBigQueryInput 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. This component is not supported with Spark 4.0.x. ## Basic settings | Properties | Description | | --- | --- | | Source type | Select the way you want tBigQueryInput to read data from
Google BigQuery:

Table: copy the whole table.
Query: write a query to select data.

| | 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.

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.
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.

| When the Source type is Table: | Properties | Description | | --- | --- | | Project ID | If your Google BigQuery service uses the
Google Cloud Platform project ID, keep this checkbox clear to allow
tBigQueryInput to read this ID from the Spark
configuration tab or the
tBigQueryConfiguration component.

If the Google BigQuery service uses
a custom ID, select this checkbox and enter the ID.

The ID of your project can be
found in the URL of the Google API Console, or by hovering your mouse
pointer over the name of the project in the BigQuery Browser
Tool. | | Dataset | Enter the name of the dataset to
which the table to be copied belongs.

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. | | Table | Enter the name of the table to be
copied. | When the Source type is Query: | Properties | Description | | --- | --- | | Query | Enter the query to be used.

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.

If the
query to be used is the legacy SQL of BigQuery, select this Use
legacy SQL checkbox. For further information about this legacy
SQL, see [Legacy SQL query reference](https://cloud.google.com/bigquery/docs/reference/legacy-sql) in the Google Cloud
documentation. | ## Usage | Usage guidance | Description | | --- | --- | | **Usage rule** | This is an input component. It sends data extracted from
BigQuery to the component that follows it.

Place a tBigQueryConfiguration
component in the same Job because it needs to use the BigQuery
configuration information provided by tBigQueryConfiguration. | | 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. |