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  • Welcome to Syntho
  • Overview
    • Get started
      • Syntho bootcamp
        • 1. What is Syntho?
        • 2. Introduction data anonymization
        • 3. Connectors & workspace creation
        • 4. PII scan
        • 5. Generators
          • Mockers
          • Maskers
          • AI synthesize
          • Calculated columns
          • Free text de-identification
        • 6. Referential integrity & foreign keys
        • 7. Workspace synchronization & validation
        • 8. Workspace & user management
        • 9. Large workloads​
        • 10. Data pre-processing
        • 11. Continuous Success
      • Prerequisites
      • Sample datasets
      • Introduction to data generators
    • Frequently asked questions
  • Setup Workspaces
    • View workspaces
    • Create a workspace
      • Connect to a database
        • PostgreSQL
        • MySQL / MariaDB
        • Oracle
        • Microsoft SQL Server
        • DB2
        • Databricks
          • Importing Data into Databricks
        • Hive
        • SAP Sybase
        • Azure Data Lake Storage (ADLS)
        • Amazon Simple Storage Service (S3)
      • Workspace modes
    • Edit a workspace
    • Duplicate a workspace
    • Transfer workspace ownership
    • Share a workspace
    • Delete a workspace
    • Workspace default settings
  • Configure a Data Generation Job
    • Configure table settings
    • Configure column settings
      • AI synthesize
        • Sequence model
          • Prepare your sequence data
        • QA report
        • Additional privacy controls
        • Cross-table relationships limitations
      • Mockers
        • Text
          • Supported languages
        • Numeric (integer)
        • Numeric (decimal)
        • Datetime
        • Other
      • Mask
        • Text
        • Numeric (integer)
        • Numeric (decimal)
        • Datetime
        • UUID
      • Duplicate
      • Exclude
      • Consistent mapping
      • Calculated columns
      • Key generators
        • Differences between key generators
      • JSON de-identification
    • Manage personally identifiable information (PII)
      • Privacy dashboard
      • Discover and de-identify PII columns
        • Identify PII columns manually
        • Automatic PII discovery with PII scanner
      • Remove columns from PII list
      • Automatic PII discovery and de-identification in free text columns
      • Supported PII & PHI entities
    • Manage foreign keys
      • Foreign key inheritance
      • Add virtual foreign keys
        • Add virtual foreign keys
        • Use foreign key scanner
        • Import foreign keys via JSON
        • Export foreign keys via JSON
      • Delete foreign keys
    • Validate and Synchronize workspace
    • View and adjust generation settings
  • Deploy Syntho
    • Introduction
      • Syntho architecture
      • Requirements
        • Requirements for Docker deployments
        • Requirements for Kubernetes deployments
      • Access Docker images
        • Online
        • Offline
    • Deploy Syntho using Docker
      • Preparations
      • Deploy using Docker Compose
      • Run the application
      • Manually saving logs
      • Updating the application
    • Deploy Syntho using Kubernetes
      • Preparations
      • Deploy Ray using Helm
        • Upgrading Ray CRDs
        • Troubleshooting
      • Deploy Syntho using Helm
      • Validate the deployment
      • Troubleshooting
      • Saving logs
      • Upgrading the applications
    • Manage users and access
      • Single Sign-On (SSO) in Azure
      • Manage admin users
      • Manage non-admin users
    • Logs and monitoring
      • Does Syntho collect any data?
      • Temporary data storage by application
  • Syntho API
    • Syntho REST API
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On this page
  • Apply mocker
  • Apply mocker via Job Configuration
  • Apply mocker via PII tab
  • Edit mock data settings
  • Available mockers
  • Supported languages

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  1. Configure a Data Generation Job
  2. Configure column settings

Mockers

PreviousCross-table relationships limitationsNextText

Last updated 15 days ago

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Mockers can be especially useful in the following situations:

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

  • To fill columns that do not contain any data yet. See .

Apply 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.

Available mockers

Supported languages

Syntho supports each mocker in multiple different languages. For the complete list of supported languages, 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.

There are various mockers available, each designed to generate mocker data based on different data types. You can explore them based on their categories, including , , , Custom Sampler, and .

Text
Numeric (integer)
Numeric (decimal)
Other
Supported languages
related FAQ question
Selecting data type
Applied steps

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 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 text.

  • 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 feature.

  • Database Type Compatibility: The return type of a mocker indicates its compatibility with specific database data types. For example, a mocker with a text return type is compatible with database types like (N)VARCHAR or TEXT, but not with a database type NUMERIC (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.

Consistent Mapping