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  • Tonic Structural User Guide
  • About Tonic Structural
    • Structural data generation workflow
    • Structural deployment types
    • Structural implementation roles
    • Structural license plans
  • Logging into Structural for the first time
  • Getting started with the Structural free trial
  • Managing your user account
  • Frequently Asked Questions
  • Tutorial videos
  • Creating and managing workspaces
    • Managing workspaces
      • Viewing your list of workspaces
      • Creating, editing, or deleting a workspace
      • Workspace configuration settings
        • Workspace identification and connection type
        • Data connection settings
        • Configuring secrets managers for database connections
        • Data generation settings
        • Enabling and configuring upsert
        • Writing output to Tonic Ephemeral
        • Writing output to a container repository
        • Advanced workspace overrides
      • About the workspace management view
      • About workspace inheritance
      • Assigning tags to a workspace
      • Exporting and importing the workspace configuration
    • Managing access to workspaces
      • Sharing workspace access
      • Transferring ownership of a workspace
    • Viewing workspace jobs and job details
  • Configuring data generation
    • Privacy Hub
    • Database View
      • Viewing and configuring tables
      • Viewing the column list
      • Displaying sample data for a column
      • Configuring an individual column
      • Configuring multiple columns
      • Identifying similar columns
      • Commenting on columns
    • Table View
    • Working with document-based data
      • Performing scans on collections
      • Using Collection View
    • Identifying sensitive data
      • Running the Structural sensitivity scan
      • Manually indicating whether a column is sensitive
      • Built-in sensitivity types that Structural detects
      • Creating and managing custom sensitivity rules
    • Table modes
    • Generator information
      • Generator summary
      • Generator reference
        • Address
        • Algebraic
        • Alphanumeric String Key
        • Array Character Scramble
        • Array JSON Mask
        • Array Regex Mask
        • ASCII Key
        • Business Name
        • Categorical
        • Character Scramble
        • Character Substitution
        • Company Name
        • Conditional
        • Constant
        • Continuous
        • Cross Table Sum
        • CSV Mask
        • Custom Categorical
        • Date Truncation
        • Email
        • Event Timestamps
        • File Name
        • Find and Replace
        • FNR
        • Geo
        • HIPAA Address
        • Hostname
        • HStore Mask
        • HTML Mask
        • Integer Key
        • International Address
        • IP Address
        • JSON Mask
        • MAC Address
        • Mongo ObjectId Key
        • Name
        • Noise Generator
        • Null
        • Numeric String Key
        • Passthrough
        • Phone
        • Random Boolean
        • Random Double
        • Random Hash
        • Random Integer
        • Random Timestamp
        • Random UUID
        • Regex Mask
        • Sequential Integer
        • Shipping Container
        • SIN
        • SSN
        • Struct Mask
        • Timestamp Shift Generator
        • Unique Email
        • URL
        • UUID Key
        • XML Mask
      • Generator characteristics
        • Enabling consistency
        • Linking generators
        • Differential privacy
        • Partitioning a column
        • Data-free generators
        • Supporting uniqueness constraints
        • Format-preserving encryption (FPE)
      • Generator types
        • Composite generators
        • Primary key generators
    • Generator assignment and configuration
      • Reviewing and applying recommended generators
      • Assigning and configuring generators
      • Document View for file connector JSON columns
      • Generator hints and tips
      • Managing generator presets
      • Configuring and using Structural data encryption
      • Custom value processors
    • Subsetting data
      • About subsetting
      • Using table filtering for data warehouses and Spark-based data connectors
      • Viewing the current subsetting configuration
      • Subsetting and foreign keys
      • Configuring subsetting
      • Viewing and managing configuration inheritance
      • Viewing the subset creation steps
      • Viewing previous subsetting data generation runs
      • Generating cohesive subset data from related databases
      • Other subsetting hints and tips
    • Viewing and adding foreign keys
    • Viewing and resolving schema changes
    • Tracking changes to workspaces, generator presets, and sensitivity rules
    • Using the Privacy Report to verify data protection
  • Running data generation
    • Running data generation jobs
      • Types of data generation
      • Data generation process
      • Running data generation manually
      • Scheduling data generation
      • Issues that prevent data generation
    • Managing data generation performance
    • Viewing and downloading container artifacts
    • Post-job scripts
    • Webhooks
  • Installing and Administering Structural
    • Structural architecture
    • Using Structural securely
    • Deploying a self-hosted Structural instance
      • Deployment checklist
      • System requirements
      • Deploying with Docker Compose
      • Deploying on Kubernetes with Helm
      • Enabling the option to write output data to a container repository
        • Setting up a Kubernetes cluster to use to write output data to a container repository
        • Required access to write destination data to a container repository
      • Entering and updating your license key
      • Setting up host integration
      • Working with the application database
      • Setting up a secret
      • Setting a custom certificate
    • Using Structural Cloud
      • Structural Cloud notes
      • Setting up and managing a Structural Cloud pay-as-you-go subscription
      • Structural Cloud onboarding
    • Managing user access to Structural
      • Structural organizations
      • Determining whether users can create accounts
      • Creating a new account in an existing organization
      • Single sign-on (SSO)
        • Structural user authentication with SSO
        • Enabling and configuring SSO on Structural Cloud
        • Synchronizing SSO groups with Structural
        • Viewing the list of SSO groups in Tonic Structural
        • AWS IAM Identity Center
        • Duo
        • GitHub
        • Google
        • Keycloak
        • Microsoft Entra ID (previously Azure Active Directory)
        • Okta
        • OpenID Connect (OIDC)
        • SAML
      • Managing Structural users
      • Managing permissions
        • About permission sets
        • Built-in permission sets
        • Available permissions
        • Viewing the lists of global and workspace permission sets
        • Configuring custom permission sets
        • Selecting default permission sets
        • Configuring access to global permission sets
        • Setting initial access to all global permissions
        • Granting Account Admin access for a Structural Cloud organization
    • Structural monitoring and logging
      • Monitoring Structural services
      • Performing health checks
      • Downloading the usage report
      • Tracking user access and permissions
      • Redacted and diagnostic (unredacted) logs
      • Data that Tonic.ai collects
      • Verifying and enabling telemetry sharing
    • Configuring environment settings
    • Updating Structural
  • Connecting to your data
    • About data connectors
    • Overview for database administrators
    • Data connector summary
    • Amazon DynamoDB
      • System requirements and limitations for DynamoDB
      • Structural differences and limitations with DynamoDB
      • Before you create a DynamoDB workspace
      • Configuring DynamoDB workspace data connections
    • Amazon EMR
      • Structural process overview for Amazon EMR
      • System requirements for Amazon EMR
      • Structural differences and limitations with Amazon EMR
      • Before you create an Amazon EMR workspace
        • Creating IAM roles for Structural and Amazon EMR
        • Creating Athena workgroups
        • Configuration for cross-account setups
      • Configuring Amazon EMR workspace data connections
    • Amazon Redshift
      • Structural process overview for Amazon Redshift
      • Structural differences and limitations with Amazon Redshift
      • Before you create an Amazon Redshift workspace
        • Required AWS instance profile permissions for Amazon Redshift
        • Setting up the AWS Lambda role for Amazon Redshift
        • AWS KMS permissions for Amazon SQS message encryption
        • Amazon Redshift-specific Structural environment settings
        • Source and destination database permissions for Amazon Redshift
      • Configuring Amazon Redshift workspace data connections
    • Databricks
      • Structural process overview for Databricks
      • System requirements for Databricks
      • Structural differences and limitations with Databricks
      • Before you create a Databricks workspace
        • Granting access to storage
        • Setting up your Databricks cluster
        • Configuring the destination database schema creation
      • Configuring Databricks workspace data connections
    • Db2 for LUW
      • System requirements for Db2 for LUW
      • Structural differences and limitations with Db2 for LUW
      • Before you create a Db2 for LUW workspace
      • Configuring Db2 for LUW workspace data connections
    • File connector
      • Overview of the file connector process
      • Supported file and content types
      • Structural differences and limitations with the file connector
      • Before you create a file connector workspace
      • Configuring the file connector storage type and output options
      • Managing file groups in a file connector workspace
      • Downloading generated file connector files
    • Google BigQuery
      • Structural differences and limitations with Google BigQuery
      • Before you create a Google BigQuery workspace
      • Configuring Google BigQuery workspace data connections
      • Resolving schema changes for de-identified views
    • MongoDB
      • System requirements for MongoDB
      • Structural differences and limitations with MongoDB
      • Configuring MongoDB workspace data connections
      • Other MongoDB hints and tips
    • MySQL
      • System requirements for MySQL
      • Before you create a MySQL workspace
      • Configuring MySQL workspace data connections
    • Oracle
      • Known limitations for Oracle schema objects
      • System requirements for Oracle
      • Structural differences and limitations with Oracle
      • Before you create an Oracle workspace
      • Configuring Oracle workspace data connections
    • PostgreSQL
      • System requirements for PostgreSQL
      • Before you create a PostgreSQL workspace
      • Configuring PostgreSQL workspace data connections
    • Salesforce
      • System requirements for Salesforce
      • Structural differences and limitations with Salesforce
      • Before you create a Salesforce workspace
      • Configuring Salesforce workspace data connections
    • Snowflake on AWS
      • Structural process overviews for Snowflake on AWS
      • Structural differences and limitations with Snowflake on AWS
      • Before you create a Snowflake on AWS workspace
        • Required AWS instance profile permissions for Snowflake on AWS
        • Other configuration for Lambda processing
        • Source and destination database permissions for Snowflake on AWS
        • Configuring whether Structural creates the Snowflake on AWS destination database schema
      • Configuring Snowflake on AWS workspace data connections
    • Snowflake on Azure
      • Structural process overview for Snowflake on Azure
      • Structural differences and limitations with Snowflake on Azure
      • Before you create a Snowflake on Azure workspace
      • Configuring Snowflake on Azure workspace data connections
    • Spark SDK
      • Structural process overview for the Spark SDK
      • Structural differences and limitations with the Spark SDK
      • Configuring Spark SDK workspace data connections
      • Using Spark to run de-identification of the data
    • SQL Server
      • System requirements for SQL Server
      • Before you create a SQL Server workspace
      • Configuring SQL Server workspace data connections
    • Yugabyte
      • System requirements for Yugabyte
      • Structural differences and limitations with Yugabyte
      • Before you create a Yugabyte workspace
      • Configuring Yugabyte workspace data connections
      • Troubleshooting Yugabyte data generation issues
  • Using the Structural API
    • About the Structural API
    • Getting an API token
    • Getting the workspace ID
    • Using the Structural API to perform tasks
      • Configure environment settings
      • Manage generator presets
        • Retrieving the list of generator presets
        • Structure of a generator preset
        • Creating a custom generator preset
        • Updating an existing generator preset
        • Deleting a generator preset
      • Manage custom sensitivity rules
      • Create a workspace
      • Connect to source and destination data
      • Manage file groups in a file connector workspace
      • Assign table modes and filters to source database tables
      • Set column sensitivity
      • Assign generators to columns
        • Getting the generator IDs and available metadata
        • Updating generator configurations
        • Structure of a generator assignment
        • Generator API reference
          • Address (AddressGenerator)
          • Algebraic (AlgebraicGenerator)
          • Alphanumeric String Key (AlphaNumericPkGenerator)
          • Array Character Scramble (ArrayTextMaskGenerator)
          • Array JSON Mask (ArrayJsonMaskGenerator)
          • Array Regex Mask (ArrayRegexMaskGenerator)
          • ASCII Key (AsciiPkGenerator)
          • Business Name (BusinessNameGenerator)
          • Categorical (CategoricalGenerator)
          • Character Scramble (TextMaskGenerator)
          • Character Substitution (StringMaskGenerator)
          • Company Name (CompanyNameGenerator)
          • Conditional (ConditionalGenerator)
          • Constant (ConstantGenerator)
          • Continuous (GaussianGenerator)
          • Cross Table Sum (CrossTableAggregateGenerator)
          • CSV Mask (CsvMaskGenerator)
          • Custom Categorical (CustomCategoricalGenerator)
          • Date Truncation (DateTruncationGenerator)
          • Email (EmailGenerator)
          • Event Timestamps (EventGenerator)
          • File Name (FileNameGenerator)
          • Find and Replace (FindAndReplaceGenerator)
          • FNR (FnrGenerator)
          • Geo (GeoGenerator)
          • HIPAA Address (HipaaAddressGenerator)
          • Hostname (HostnameGenerator)
          • HStore Mask (HStoreMaskGenerator)
          • HTML Mask (HtmlMaskGenerator)
          • Integer Key (IntegerPkGenerator)
          • International Address (InternationalAddressGenerator)
          • IP Address (IPAddressGenerator)
          • JSON Mask (JsonMaskGenerator)
          • MAC Address (MACAddressGenerator)
          • Mongo ObjectId Key (ObjectIdPkGenerator)
          • Name (NameGenerator)
          • Noise Generator (NoiseGenerator)
          • Null (NullGenerator)
          • Numeric String Key (NumericStringPkGenerator)
          • Passthrough (PassthroughGenerator)
          • Phone (USPhoneNumberGenerator)
          • Random Boolean (RandomBooleanGenerator)
          • Random Double (RandomDoubleGenerator)
          • Random Hash (RandomStringGenerator)
          • Random Integer (RandomIntegerGenerator)
          • Random Timestamp (RandomTimestampGenerator)
          • Random UUID (UUIDGenerator)
          • Regex Mask (RegexMaskGenerator)
          • Sequential Integer (UniqueIntegerGenerator)
          • Shipping Container (ShippingContainerGenerator)
          • SIN (SINGenerator)
          • SSN (SsnGenerator)
          • Struct Mask (StructMaskGenerator)
          • Timestamp Shift (TimestampShiftGenerator)
          • Unique Email (UniqueEmailGenerator)
          • URL (UrlGenerator)
          • UUID Key (UuidPkGenerator)
          • XML Mask (XmlMaskGenerator)
      • Configure subsetting
      • Check for and resolve schema changes
      • Run data generation jobs
      • Schedule data generation jobs
    • Example script: Starting a data generation job
    • Example script: Polling for a job status and creating a Docker package
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On this page
  • Selecting the Ephemeral instance type
  • Writing output to Ephemeral Cloud
  • Writing output to self-hosted Ephemeral
  • Selecting the image for Oracle
  • Configuring advanced settings for the snapshot
  • Providing a snapshot name and description
  • Setting the pod resources for the snapshot
  • Setting the size allocation for the snapshot
  • Indicating whether to keep the temporary Ephemeral database
  • Providing a customization file for MySQL or PostgreSQL

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  1. Creating and managing workspaces
  2. Managing workspaces
  3. Workspace configuration settings

Writing output to Tonic Ephemeral

Last updated 2 months ago

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Only available for PostgreSQL, MySQL, SQL Server, and Oracle.

Not compatible with upsert.

Not compatible with Preserve Destination or Incremental table modes.

Tonic Ephemeral is a separate Tonic.ai product that allows you to create temporary databases to use for testing and demos. For more information about Ephemeral, go to the .

If Ephemeral supports your workspace database type, then you can write the destination data to a snapshot in Ephemeral. You can then use the snapshot to start Ephemeral databases.

To write the transformed data to Ephemeral, under Destination Settings, click Ephemeral Database.

Selecting the Ephemeral instance type

Structural can write the data snapshot to either Ephemeral Cloud or to a self-hosted instance of Ephemeral. By default, Structural writes the data snapshot to Ephemeral Cloud.

All workspaces on the same self-hosted Structural instance or in the same Structural Cloud organization must write to the same instance of Ephemeral. When you change the Ephemeral output configuration in one workspace, it is automatically changed in other workspaces that write to Ephemeral.

Writing output to Ephemeral Cloud

Structural writes the snapshot to the Ephemeral Cloud account for the user who runs the data generation job.

  • If that user has an Ephemeral account on Ephemeral Cloud, then Structural uses that account.

  • If the user does not have an account, then Structural creates an account for them.

On Structural Cloud, when you save the workspace, if you do not have an Ephemeral Cloud account, then an Ephemeral Cloud account is created for you.

When Structural creates an Ephemeral account, if the user belongs to an existing Ephemeral Cloud organization, then the account is added to the organization. Otherwise, the account is a two-week free trial account.

For a self-hosted Structural workspace, you must provide an API key from an existing Ephemeral Cloud account.

To write a snapshot to Ephemeral Cloud:

  1. Click Tonic Ephemeral cloud.

  2. If you are on a self-hosted instance of Structural:

    1. In the API Key field, provide an Ephemeral API key from your Ephemeral account.

    2. To test the connection, click Test Connection.

Writing output to self-hosted Ephemeral

When you write to a self-hosted instance of Ephemeral, then you must always provide an Ephemeral API key.

To write the snapshot to a self-hosted instance of Ephemeral:

  1. Click Tonic Ephemeral self-hosted.

  2. In the API Key field, provide an Ephemeral API key from your Ephemeral account. Structural writes the snapshot to the Ephemeral account that is associated with the API key.

  3. In the Tonic Ephemeral URL field, provide the URL to your self-hosted Ephemeral instance.

  4. To test the connection, click Test Connection.

Selecting the image for Oracle

For Oracle, you select the base image to use to create the data snapshot.

If you write to Ephemeral Cloud, then you must use the Oracle 23c base image that comes with Ephemeral. This image has the following limitations:

  • A maximum of 12GB of user data

  • A maximum of 2CPU cores and 2GB of RAM

If you write to a self-hosted instance of Ephemeral, then you can also select a custom image that you created in Ephemeral.

Configuring advanced settings for the snapshot

If you do not configure any advanced settings, then:

  • The snapshot uses the same name as the workspace, and has no description.

  • The snapshot size allocation is determined by the source data size.

  • Structural discards the temporary Ephemeral database that is created during the data generation.

To change any of these settings, click Advanced settings.

Providing a snapshot name and description

By default, the snapshot name uses the workspace name.

When you run data generation, if a snapshot with the same name already exists in Ephemeral, then Structural overwrites that snapshot with the new snapshot.

Under Advanced settings:

  1. In the Snapshot name field, provide the name of the snapshot. The snapshot name can use the following placeholder values to help identify the snapshot:

    • {workspaceName} - Inserts the name of the workspace.

    • {workspaceId} - Inserts the identifier of the workspace.

    • {jobId} - Inserts the identifier of the data generation job that created the snapshot.

    • {timestamp} - Inserts the timestamp when the snapshot was created.

    Including the job ID or timestamp ensures that a data generation job does not overwrite a previous snapshot.

  2. Optionally, in the Snapshot description field, provide a longer description of the snapshot.

Setting the pod resources for the snapshot

By default, the resources used for the snapshot are based on the size of the source data.

  • For source data that is 25 GB or less, Nano is used.

  • For source data larger than 25 GB, Micro is used.

To select a specific option:

  1. Toggle Custom pod resources to the on position.

  2. From the dropdown list, select the option to use for the combination of vCPUs and memory:

    • Nano - 0.125 vCPU with 0.5 GB RAM

    • Micro - 0.5 vCPU with 2 GB RAM

    • Small - 1 vCPU with 4 GB RAM

    • Medium - 2 vCPU with 8 GB RAM

    • Large - 4 vCPU with 16 GB RAM

Setting the size allocation for the snapshot

By default, the Ephemeral size allocation for the snapshot is based on the size of the source data.

To instead provide a custom data size allocation, under Advanced settings:

  1. Toggle Custom data size allocation to the on position.

  2. In the field, enter the size allocation in gigabytes.

Indicating whether to keep the temporary Ephemeral database

When Structural creates the Ephemeral snapshot, it creates a temporary Ephemeral database.

By default, Structural deletes that database when the data generation is complete.

To instead keep the database, under Advanced settings, toggle Keep database active in Tonic Ephemeral after data generation to the on position.

Providing a customization file for MySQL or PostgreSQL

For a MySQL or PostgreSQL workspace, you can provide a customization file that helps to ensure that the temporary Ephemeral database is configured correctly.

To provide the customization details:

  1. Toggle Use custom configuration to the on position.

  2. In the text area, paste the contents of the customization file.

For information about how to create and manage custom images for Oracle, go to .

In Ephemeral Cloud, by default, databases are publicly accessible. To limit database access, you can configure Ephemeral Cloud with an IP allowlist for your organization. For more information, go to in the Ephemeral documentation.

Configuring an allowlist for Ephemeral Cloud database connections
Managing custom images in the Ephemeral documentation
Ephemeral documentation
Writing Structural data generation output to a Tonic Ephemeral data snapshot