These properties are used to configure tPatternMasking running in the Spark Batch Job framework.
The Spark Batch tPatternMasking 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.
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 read-only 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.
Define in the table what fields to change and how to change them:
Column to mask: Select the column from the input flow for which you want to generate similar data by modifying its values.
You can mask data from different columns but you need to follow the order of the fields you want to mask.
Each column is processed sequentially, meaning that data masking 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
The component uses Format-Preserving Encryption (FPE) methods to generate masked output values in the same format as the input values.
The FPE methods are bijective methods, except when using tweaks.
The Basic method is the default algorithm.
Note: As the masking methods are stronger, it is recommended to use the FF1 algorithms rather than the Basic method.
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.
You can use those methods only if the number of possible values the component can generate from the input pattern is greater than or equal to 1,000,000.
Note: 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.
The FF1 with AES and FF1 with SHA-2 methods require a password to be specified in the Password for FF1 methods field of the Advanced settings to generate unique masked values.
Password for FF1 methods
Set the password required for the FF1 with AES and FF1 with SHA-2 methods to generate unique masked values. If the password is not set, a random password is created at each Job execution. When using the FF1 with AES and FF1 with SHA-2 methods and a password, the seed from the Seed for random generator field is not used.
|Use tweaks with FF1 Encryption
Select this check box to use tweaks. A unique tweak is generated for each record and applies to all data of a record.
If bijective masking is necessary, do not use this functionality. For more information about tweaks, see the data masking functions.
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.
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
If the input is
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.
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.
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.