Mockers

Mockers can be especially useful in the following situations:

1. To fill columns that contain directly identifiable information, such as Personally Identifiable Information (PII).

2. To fill columns that do not contain any data yet. Also, see: related FAQ question.

Apply a mocker

You can apply mockers in two different manners, via the Job Configuration tab, or via the PII tab.

Apply mocker via Job Configuration

You can apply a mocker on a column via the Job Configuration tab as follows:

1. Open your Workspace.

2. On the Job Configuration tab, select the column icon on the top left of the column where you want to apply a mocker.

3. Under Column settings > Generation Method, select Mocker to view the list of available mockers.

4. Select the Mocker that you wish to apply from the dropdown list of available mockers.

5. Set the relevant mocker parameters.

6. Select Confirm.

Apply mocker via PII tab

You can apply a mocker on a column via the PII tab as follows: Identify PII columns manually.

Edit mock data settings

To edit any mock data settings you have applied previously:

1. Open your Workspace.

2. Now you can either:

   1. On the Job Configuration tab, select the column icon on the top left of the column where you want to edit a mocker.

   2. On the Job Configuration tab, under Applied steps, select the Edit icon next to the column name where you want to edit a mocker.

   3. On the PII tab, select the Edit icon behind the column where you want to edit a mocker.

3. Under Generation Method, define the parameters that you want to change.

4. Select Confirm.

Mocker parameters

When setting the parameters for a mocker, you have various options to tailor the data according to your needs. Here are the main mocker parameters that are shared across mockers:

1. Consistent mapping

   • Description: Enabling the consistent mapping allows you to generate the same mock data values for a given set of original data values every time the mocker is applied.

   • Options:

     • Enable: Turn on to consistently generate the same mock values for the same some original values.

     • Disable: Turn off consistent mapping to generate random mock data.

   • Considerations: It is possible that same original input value is consistently mapped to the same output mock value. For example, John and Mike in the original data can possibly both be mapped to Eric in the mock data.

   • Usage: When you need to consistently generate the same mock values for testing or demonstration purposes.

   For more information on consistent mapping, please check Consistent mapping.

2. Unique

   • Description: This option ensures that only unique values are generated in the specified column.

   • Options:

     • Enable: Turn on to generate only unique values.

     • Disable: Turn off to allow for repeated values.

   • Considerations: When the range of possible values is small, it may become impossible to generate unique values after a certain number of iterations.

   • Usage: When mocking columns that should contain distinct values like IDs or usernames.

Available mockers

Advanced mockers

Supported languages

Syntho supports each mocker in multiple different languages. For the complete list of supported languaged, see the following section:

The default language used by each mocker is English (United States). In case a language is not available for a particular mocker, the mocker will revert back to this language.

Limitations & Considerations

• Constraints on Key Columns: Mockers cannot be applied to primary key or foreign key columns.

• Column-by-Column Operation: Mockers function on individual columns. At this point, they can't be used to preserve logical relationships across multiple columns.

• Automatically Cutoff Values: The Syntho platform automatically cuts off generated mock string/text values based on the data type's supported length. For example, a mocker applied on an NVARCHAR(5) column, will cutoff all values beyond the first 5 characters of the string.

• Automatically Clip Values: The Syntho platform automatically clips numerical values that exceed the maximum or minimum size, to the largest or smallest value supported by the data type, respectively.

• No Link with Original Records: Mockers do not link back to the original data records, enhancing privacy but potentially reducing the usefulness of the data. If you want to retain the link with the original values, you can enable the Consistent Mapping feature.

• Database Type Compatibility: The return type of a mocker indicates its compatibility with specific database data types. For example, a mocker with a String return type is compatible with database types like (N)VARCHAR or TEXT, but not with a database type INTEGER. It is important to this into account when applying mockers on your columns to prevent your data generation job to fail.

Being aware of these limitations and considerations will help you effectively use mockers while understanding their constraints.