Skip to main content Skip to complementary content

tStandardizePhoneNumber properties for Apache Spark Batch

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

The Spark Batch tStandardizePhoneNumber component belongs to the Data Quality family.

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

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.

This component provides default columns. For further information, see section Default columns.


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.

Phone number

Select the column holding the phone numbers of interest from the input data.

Country code

Select the column holding the country codes (ISO 2) from the input data.

Information noteNote:

The input data processed by this component must be able to provide the two-letter ISO country codes alongside the corresponding phone numbers of interest.


Select this check box to set a custom country code (ISO 2). Once selected, it disables the Country code field and gives priority to the customized country code for phone number standardization.

For example, if the input data provides a set of phone numbers with a wrong country code or alternatively with no country code, then you can select this check box and type in the country code you need for standardization.

Phone number format for output

Select the format to be used to standardize the phone numbers of interest. The available options are:

- E164

- International

- National

Advanced settings

Avoid comparison

Select this check box to deactivate the comparison performed between the input and the output data at runtime. This can accelerate the execution process of the Job using this component.

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 more information about variables, see Using contexts and variables.


Usage rule

This component is used as an intermediate step.

This component, along with the Spark Batch component Palette it belongs to, appears only when you are creating a Spark Batch Job.

Note that in this documentation, unless otherwise explicitly stated, a scenario presents only Standard Jobs, that is to say traditional Talend data integration Jobs.

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.

Did this page help you?

If you find any issues with this page or its content – a typo, a missing step, or a technical error – let us know how we can improve!