tPatternUnmasking properties for Apache Spark Streaming
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.
A schema is a row description. It defines the number of fields
The output schema of this component contains one
Built-In: You create and store the schema locally for this component
Repository: You have already created the schema and stored it in the
Define in the table what fields to unmask and how to
Use the same settings for the Field
Column to unmask:
Each column is processed sequentially, meaning that data
In a colum, each data field is a fixed length field,
For fixed length fields, each value must contain the
In a column, the last Enumeration or Enumeration
For variable length fields, each value might not always
Field type: Select
the field type the data belongs to.
In the Values,
When the input data is invalid, meaning that a value does not match the pattern defined in
From this list, select the Format-Preserving Encryption
The FF1 with AES
Java 8u161 is the minimum required version to use the
Password for FF1
To unmask data, the FF1 with AES
If tweaks have been generated while encrypting
|Column containing tweaks
Available when the Use tweaks check box is selected. Select the column
Seed for random
Set a random number if you want to generate
If you do not set the seed, the component
Select the encoding from the list or select Custom and define it manually. If you select Custom and leave the field empty, the supported
When you set Field
Output the original
Select this check box to output original data rows in addition to the
Should Null input return
This check box is selected by
If the input is
Should EMPTY input return
When this check box is selected, empty values are left unchanged in
|Send invalid data to “Invalid” output
This check box is selected by default.
Invalid data are any values that do not match the pattern.
Select this check box to gather the Job processing metadata at the Job level
This component is used as an intermediate step.
This component, along with the Spark Batch component Palette it belongs to,
Note that in this documentation, unless otherwise explicitly stated, a
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.