# tPatternUnmasking properties for Apache Spark Batch These properties are used to configure tPatternUnmasking running in the Spark Batch Job framework. The Spark Batch 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:



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.



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.




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:



This connection is effective on a per-Job basis. |