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
  • Standard mockers
  • JSON
  • Custom sampler

Was this helpful?

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

Other

PreviousDatetimeNextMask

Last updated 2 months ago

Was this helpful?

Below is a list of available other types of mockers.

Standard mockers

Mocker
Return Type
Description

Boolean

bool

Either True or False.

Latitude longitude

geo

A tuple of (latitude, longitude) coordinates.

Pybool

bool

A randomly generated Python boolean value.

UUID

uuid

A string representing a UUID (universally unique identifier) in v4 format.

JSON

This mocker uses Faker to generate randomized, complex and a text representation of JSON data, simulating real-world scenarios with specified data types across fields.

Parameters

  • Data columns: Specification for the data structure

  • Number of rows: Number of rows the returned

  • cls(json.JSONEncoder): Optional JSON encoder to use for non-standard objects such as datetimes

  • Consistent mapping: JSON supports .

Note: For more information, refer to the .

Example

If you configure:

{"Name":"name", "Address":"address"}

The results will be:

{"Name": "Rebecca Crawford", "Address": "USCGC Harrell\nFPO AP 64614"},
{"Name": "Mark Ayala", "Address": "979 Clay Vista Apt. 789\nNew Zacharymouth, NC 40691"},
{"Name": "Stephanie Chaney", "Address": "852 Debbie Valley\nBrittanystad, FM 41302"}

Custom sampler

Generates random text, numeric (integer or decimal) object depending on provided values, sampled from a provided list of input values.

Parameters

  • Values (comma separated values): The list of values to sample from.

  • ... (File upload): Allows users to import predefined lists of values from .csv or .txt files. The uploaded file must be within 2MB and contain a maximum of 5000 characters.

Example

If you configure:

values1, values2, values3

1, 25, 99

0.1, 2.5, 99

The results will be:

values3,
values1,
values2,
...

99,
25,
1,
...

99.0,
0.1,
2.5,
...

Consistent mapping: Custom sampler supports .

consistent mapping
Faker documentation
consistent mapping