LogoLogo
Go to Syntho.AI
English
English
  • 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. AI synthesis: Data pre-processing when using
      • Prerequisites
      • Sample datasets
      • Introduction to data generators
      • AI-generated synthetic data
    • 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
      • Mock
        • 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
      • Backup
    • 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
      • Backup
    • 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
Powered by GitBook
On this page

Was this helpful?

  1. Overview
  2. Get started
  3. Syntho bootcamp
  4. 5. Generators

Mockers

Previous5. GeneratorsNextMaskers

Last updated 16 days ago

Was this helpful?

are used to generate entirely new, random values for a column. They are ideal when privacy is the top priority, and there’s no need to maintain a link to the original data values.

When to use

  • To fill columns that contain directly identifiable information (PII)

  • To populate empty columns

  • When format matters, but data relationships do not

When not to use

  • When relationships with original data must be preserved

  • For key columns (e.g., primary or foreign keys)

  • When data correlations or dependencies matter

  • When maintaining statistical properties is important

Available mockers

Mockers are grouped by data type:

  • Numeric (and )

Interactive guide: How to apply a mocker

Follow the interactive guide below to apply a mocker.

Mockers
Text
integer
decimal
Datetime
Other

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