tPatternMasking properties for Apache Spark Batch - 7.2

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 original record or a substitute record 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 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".

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 is not included in the defined range, date range or in the enumeration, the generated value is null.

Method

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.

The Basic method is the default algorithm.

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.

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.

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.

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.

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.