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Tonic User Guide
About Tonic
Getting started with Tonic
Managing your Tonic account
Frequently Asked Questions
Creating and managing workspaces
Managing workspaces
Managing access to workspaces
Viewing workspace jobs and job details
Configuring data generation
Privacy Hub
Database View
Table View
Identifying sensitive data
Table modes
Generators
Generator summary
Generator reference
Generator characteristics
Enabling consistency
Linking generators
Differential privacy
Data-free generators
Partitioning a column
Supporting uniqueness constraints
Format-preserving encryption (FPE)
Generator types
Assigning and configuring generators
Generator hints and tips
Managing generator presets
Configuring and using Tonic data encryption
Custom value processors
Subsetting data
Viewing and adding foreign keys
Viewing and resolving schema changes
Tracking changes to workspaces and generator presets
Using the Privacy Report to verify data protection
Running data generation
Running a data generation job
Managing Tonic data generation performance
Post-job scripts
Webhooks
Configuring data science mode
Data science mode prerequisites
Viewing the list of models
Creating a model configuration
Configuring a model
Viewing, editing, and deleting a model
Training and exporting data models
Training a model
Reviewing the training results
Exporting a model
Installing and Administering Tonic
Tonic architecture
Using Tonic securely
Deploying a self-hosted Tonic instance
Setting up and managing a Tonic Cloud pay-as-you-go subscription
Managing user access to Tonic
Tonic monitoring and logging
Setting environment variables
Updating Tonic
Connecting to your data
About data connectors
Overview for database administrators
Data connector summary
Amazon EMR
Amazon Redshift
Databricks
File connector
Google BigQuery
MongoDB
MySQL
Oracle
PostgreSQL
Snowflake on AWS
Snowflake on Azure
Spark SDK
Spark with Livy
SQL Server
Using the Tonic API
About the Tonic API
Getting an API token
Getting the workspace ID
Using the Tonic API to perform tasks
Example script: Starting a data generation job
Example script: Polling for a job status and creating a Docker package
Other resources
Release notes
Tonic tutorial videos
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Generator characteristics
When you consider which generator to use, it helps to be familiar with these generator characteristics.
Consistency
Map the same input values to the same output values across multiple columns, tables, and databases.
Linking
Identify columns that use the same generator and that are inter-dependent or correlated.
Differential privacy
Ensures that the output does not reveal anything that is attributable to a specific member of the source data.
Data-free generators
Indicates that the generator output is completely unrelated to the input.
Column partitioning
Base the value of a column on other related columns.
Uniqueness constraints
Generators that you can use on columns that have uniqueness constraints.
Format-preserving encryption (FPE)
Encrypts data in such a way that the output is in the same format as the input.
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Generator reference
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Enabling consistency
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