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.

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 does not match the pattern defined in
the component, 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, except when
using tweaks.

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.

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 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, along with the Spark Streaming component Palette it belongs to, appears
only when you are creating a Spark Streaming Job.

This component is used as an intermediate step.

You need to use the Spark Configuration tab in the
Run view to define the connection to a given Spark cluster
for the whole Job.

This connection is effective on a per-Job basis.

For further information about a
Talend
Spark Streaming Job, see the sections
describing how to create, convert and configure a
Talend
Spark Streaming Job of the

Talend Open Studio for Big Data Getting Started Guide
.

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.