tBigQueryOutput Standard properties - 7.2

Google BigQuery

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Data Governance > Third-party systems > Cloud storages > Google BigQuery components
Data Quality and Preparation > Third-party systems > Cloud storages > Google BigQuery components
Design and Development > Third-party systems > Cloud storages > Google BigQuery components

These properties are used to configure tBigQueryOutput running in the Standard Job framework.

The Standard tBigQueryOutput component belongs to the Big Data family.

The component in this framework is available in all Talend products.

Basic settings

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.

Property type

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.

Local filename

Browse to, or enter the path to the file you want to write the received data in.


Select this check box to add rows to the existing data in the file specified in Local filename.

Authentication mode Select the mode to be used to authenticate to your project.
  • OAuth 2.0: authenticate the access using OAuth credentials. When selecting this mode, the parameters to be defined in the Basic settings view are Client ID, Client secret and Authorization code.
  • Service account: authenticate using a Google account that is associated with your Google Cloud Platform project. When selecting this mode, the parameter to be defined in the Basic settings view is Service account credentials file.
Service account credentials file Enter the path to the credentials file created for the service account to be used. This file must be stored in the machine in which your Talend Job is actually launched and executed.

For further information about how to create a Google service account and obtain the credentials file, see Getting Started with Authentication from the Google documentation.

Client ID and Client secret

Paste the client ID and the client secret, both created and viewable on the API Access tab view of the project hosting the Google BigQuery service and the Cloud Storage service you need to use.

To enter the client secret, click the [...] button next to the client secret field, and then in the pop-up dialog box enter the client secret between double quotes and click OK to save the settings.

Project ID

Paste the ID of the project hosting the Google BigQuery service you need to use.

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.

Authorization code

Paste the authorization code provided by Google for the access you are building.

To obtain the authorization code, you need to execute the Job using this component and when this Job pauses execution to print out an URL address, you navigate to this address to copy the authorization code displayed.


Enter the name of the dataset you need to transfer data to.


Enter the name of the table you need to transfer data to.

If this table does not exist, select the Create the table if it doesn't exist check box.

Action on data

Select the action to be performed from the drop-down list when transferring data to the target table. The action may be:

  • Truncate: it empties the contents of the table and repopulates it with the transferred data.

  • Append: it adds rows to the existing data in the table.

  • Empty: it populates the empty table.

Access key and Secret key

Paste the authentication information obtained from Google for making requests to Google Cloud Storage.

To enter the secret key, click the [...] button next to the secret key field, and then in the pop-up dialog box enter the password between double quotes and click OK to save the settings.

These keys can be consulted on the Interoperable Access tab view under the Google Cloud Storage tab of the project.


Enter the name of the bucket, the Google Cloud Storage container, which holds the data to be transferred to Google BigQuery.


Enter the directory of the data stored on Google Cloud Storage and to be transferred to Google BigQuery. This data must be stored directly under the bucket root. For example, enter gs://my_bucket/my_file.csv.

If the data is not on Google Cloud Storage, this directory is used as the intermediate destination before the data is transferred to Google BigQuery.

Note that this file name must be identical with the name of the file specified in the Local filename field.


Set values to ignore the header of the transferred data. For example, enter 0 to ignore no rows for the data without header and set 1 for the data with header at the first row.

Die on error

This check box is cleared by default, meaning to skip the row on error and to complete the process for error-free rows.

Advanced settings

token properties File Name

Enter the path to, or browse to the refresh token file you need to use.

At the first Job execution using the Authorization code you have obtained from Google BigQuery, the value in this field is the directory and the name of that refresh token file to be created and used; if that token file has been created and you need to reuse it, you have to specify its directory and file name in this field.

With only the token file name entered, Talend Studio considers the directory of that token file to be the root of the Studio folder.

For further information about the refresh token, see the manual of Google BigQuery.

Field Separator

Enter a character, a string, or a regular expression to separate fields for the transferred data.

Drop table if exists

Select the Drop table if exists check box to remove the table specified in the Table field, if this table already exists.

Create directory if not exists

Select this check box to create the directory you defined in the File field for Google Cloud Storage, if it does not exist.

Custom the flush buffer size

Enter the number of rows to be processed before the memory is freed.

Check disk space

Select this check box to throw an exception during execution if the disk is full.


Select the encoding from the list or select Custom and define it manually. This field is compulsory for database data handling. The supported encodings depend on the JVM that you are using. For more information, see https://docs.oracle.com.

tStatCatcher Statistics

Select this check box to collect the log data at the component level.

Global Variables

Global Variables

ERROR_MESSAGE: the error message generated by the component when an error occurs. This is an After variable and it returns a string. This variable functions only if the Die on error check box is cleared, if the component has this check box.

A Flow variable functions during the execution of a component while an After variable functions after the execution of the component.

To fill up a field or expression with a variable, press Ctrl + Space to access the variable list and choose the variable to use from it.

For further information about variables, see Talend Studio User Guide.


Usage rule

This is an output component used at the end of a Job. It receives data from its preceding component such as tFileInputDelimited, tMap or tMysqlInput.

This component automatically detects and supports both multi-regional locations and regional locations. When using the regional locations, the buckets and the datasets to be used must be in the same locations.