tPatternUnmasking properties for Apache Spark Streaming - 7.3

Data privacy

Version
7.3
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Data Governance > Third-party systems > Data Quality components > Data privacy components
Data Quality and Preparation > Third-party systems > Data Quality components > Data privacy components
Design and Development > Third-party systems > Data Quality components > Data privacy components

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

The Spark Streaming tPatternUnmasking 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 masked or and original 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.

Modifications

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

Use the same settings for the Field type, Values, Path, Range and Date Range columns as the ones used for masking the input data with the tPatternMasking component.

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

Each column is processed sequentially, meaning that data unmasking operations will be performed on the data from the first column, the second column, and so on.

In a colum, each data field is a fixed length field, except the last data field.

For fixed length fields, each value must contain the same number of characters, for example: "30001,30002,30003" or "FR,EN".

In a column, the last Enumeration or Enumeration from file data field is a variable length field.

For variable length fields, each value might not always contain the same number of characters, for example: "30001,300023,30003" or "FR,ENG".

Field type: Select the field type the data belongs to.
  • Interval: When selected, set a range of numeric values used for masking purposes in the Range field, using the following syntax: "<min>,<max>".

    The number of unmasked characters from the input data corresponds to the number of characters of the maximum value.

    For example, "1,999" will be interpreted as "001,999", which means that three characters from the input data will be masked by a value randomly selected from the defined range of values.

  • Enumeration: When selected, enter a comma-separated list of values to be used for masking data in the Values field, using the following syntax: "value1,value2,value3".

  • Enumeration from file: When selected, set the path to the CSV file containing a list of values used for masking data in the Path field. The file must contain one value per row and each value must be unique.
  • Date pattern (YYYYMMDD): When selected, set a range of years in the Date Range field, using the following syntax: "<min_year>,<max_year>".

    Years can only have four digits, for example: "1900,2100".

    The input dates to be masked must follow the YYYYMMDD pattern, for example: 20180101.

    For example, if the input date is 20180101 and the value in the Date Range is "1900,2100", 19221221 could be the output date.

In the Values, Path, Range and Date Range, values must be enclosed in double quotes.

When the input data is invalid, meaning that a value does not match the pattern defined in the component, the generated value is null.

Advanced settings

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.

Password for FF1 methods

To unmask data, the FF1 with AES and FF1 with SHA-2 methods require the password specified in the Password for FF1 methods field when the data was masked with the tPatternMasking component.

Use tweaks

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

Column containing 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.

Seed for random generator

Set a random number if you want to generate the same sample of substitute data in each execution of the Job. The seed is not set by default.

If you do not set the seed, the component creates a new random seed for each Job execution. Repeating the execution with a different seed will result in a different sample being generated.

Encoding

Select the encoding from the list or select Custom and define it manually. If you select Custom and leave the field empty, the supported encodings depend on the JVM that you are using. This field is compulsory for the file encoding.

When you set Field type to Enumeration from file, define the file path in Path (CSV File).

Output the original row?

Select this check box to output original data rows in addition to the substitute data. Outputting both the original and substitute data 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, the component 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.

If the input is null, the Generate Sequence function will not return null, even if the check box is selected.

Should EMPTY input return EMPTY?

When this check box is selected, empty values are left unchanged in the output data. 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.
Invalid data are any values that do not match the pattern.

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

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 Qubole, add a tS3Configuration to your Job to write your actual business data in the S3 system with Qubole. Without tS3Configuration, this business data is written in the Qubole HDFS system and destroyed once you shut down your cluster.
    • 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 tHDFSConfiguration.

  • Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration or tS3Configuration.

    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.