tPatternUnmasking properties for Apache Spark Streaming

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".

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.

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 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 tPatternMasking 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.

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.

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.

Null input returns null

From Talend Studio R2024-08 onwards, when Null input returns null is selected and the input data is null, the masking function is not applied, null is returned and the input data are sent to the main flow.

Empty input returns an empty output

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.

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

This connection is effective on a per-Job basis.