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tDataUnmasking properties for Apache Spark Streaming

These properties are used to configure tDataUnmasking running in the Spark Streaming Job framework.

The Spark Streaming tDataUnmasking component belongs to the Data Quality family.

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 Sync columns to retrieve the schema from the previous component connected in the Job.

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.

The output schema of this component contains one read-only column, ORIGINAL_MARK. This column identifies by true or false if the record is an original record or a substitute record respectively.


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.


Define in the table what fields to unmask and how to unmask them:

Input Column: Select the column from the input flow that contains the data to be unmasked.

You can unmask all data masked with tDataMasking using the FF1 with AES or FF1 with SHA-2 method combined with a user-defined password.

These modifications are based on the function you select in the Function column.

Category: select a category of unmasking functions from the list.

Function: Select the function that will unmask data.

The functions you can select from the Function list depend on the data type of the input column.

Method: From this list, select the Format-Preserving Encryption (FPE) algorithm that was used to mask data, FF1 with AES or FF1 with SHA-2:

The FF1 with AES method is based on the Advanced Encryption Standard in CBC mode. The FF1 with SHA-2 method depends on the secure hash function HMAC-256.

Java 8u161 is the minimum required version to use the FF1 with AES method. To be able to use this FPE method with Java versions earlier than 8u161, download the Java Cryptography Extension (JCE) unlimited strength jurisdiction policy files from Oracle website.

To unmask data, the FF1 with AES and FF1 with SHA-2 methods require the password specified in Password or 256-bit key for FF1 methods when the data was masked with the tDataMasking component.

When using the Character handling functions, such as Replace all, Replace characters between two positions, Replace all digits with FPE methods, you must select an alphabet.

From the Alphabet list, select the alphabet used to mask data with the tDataMasking component.

Extra Parameter: This field is used by some of the functions, it will be disabled when not applicable. When applicable, enter a number or a letter to decide the behavior of the function you have selected.

Keep format: this function is only used on Strings. Select this check box to keep the input format when using the Bank Account Unmasking, Credit Card Unmasking, Phone Unmasking and SSN Unmasking categories. That is to say, if there are spaces, dots ('.'), hyphens ('-') or slashes ('/') in the input, those characters are kept in the output. If you select this check box when using Phone Unmasking functions, the characters that are not numbers from the input are copied to the output as is.

Advanced settings

FF1 settings

Password or 256-bit key for FF1 methods: To unmask data, the FF1 with AES and FF1 with SHA-2 methods require the password or secret key specified in Password or 256-bit key for FF1 methods when the data was masked with the tDataMasking component.

Use tweaks: If tweaks have been generated while masking the data, select this check box. When selected, the Column containing tweaks list is displayed. A tweak allows to unmask all data of a record.

Column containing the tweaks: Available when the Use tweaks check box is selected. Select the column that contains the tweaks. If you do not see it, make sure you have declared in the input component the tweaks generated by the masking component.

Key derivation function : Select the same key derivation function as to mask the data. By default, PBKDF2 with 300,000 iterations is selected.

Output the original row

Select this check box to output masked data rows in addition to the original data. Having both data rows can be useful in debug or test processes.

Should null input return null

This check box is selected by default. When selected, the component outputs null when input values are null. Otherwise, it returns the default value when the input is null, that is an empty string for string values, 0 for numeric values and the current date for date values.

Should empty input return empty

When this check box is selected, the component returns the input values if they are empty. Otherwise, the selected functions are applied to the input data.

Send invalid data to "Invalid" output flow
This check box is selected by default.
  • Selected: When the data can be unmasked, they are sent to the main flow. Otherwise, the data are sent to the "Invalid" output flow.
  • Cleared: The data are sent to the main flow.
The data are considered invalid when:

tStat Catcher Statistics

Select this check box to gather the Job processing metadata at the Job level as well as at each component level.


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.

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