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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
  • Schema changes that Structural detects
  • Conflicting schema issues
  • Non-conflicting schema changes
  • Schedule for schema change scans
  • Configuring schema change detection
  • Preventing data generation when there are schema changes
  • Viewing the unaddressed schema changes
  • Schema change information on the Workspaces view
  • Displaying the Schema Changes view
  • Resolving conflicting schema issues
  • Information in the issues list
  • Resolving a single issue
  • Resolving all of the issues
  • How Structural resolves conflicting issues
  • Dismissing non-conflicting schema changes
  • Information in the notifications list
  • Dismissing an individual notification
  • Dismissing all of the notifications
  • Rerunning the sensitivity scan to check for new sensitive data

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  1. Configuring data generation

Viewing and resolving schema changes

Required workspace permission: Resolve schema change warnings

A database schema can evolve over time. For example, a table is added, a column is removed, or a column data type changes.

It's important that you are aware of these changes and that you update your data generation configuration to address these changes.

In some cases, if you don't update the configuration, then sensitive data might be leaked. For example, when a new column is added, by default the generator is Passthrough. If you do not assign a different generator, then the next time you generate data, the source data is copied to the destination database without being masked.

In other cases, the data generation fails if you don't update the configuration. For example, a column data type changes from integer to string. If the column is assigned the Random Integer generator, the data generation fails.

Tonic Structural monitors your source database to look for changes to the data schema. It alerts you to those changes, and allows you to acknowledge or resolve the changes. You can also configure your workspace so that you cannot generate data when there are unacknowledged or unresolved schema changes.

Schema changes that Structural detects

Structural detects the following schema changes.

Conflicting schema issues

Conflicting schema issues can cause the data generation to fail if they are not resolved.

Structural detects the following conflicting schema issues:

  • Table that has configured generators is removed from the schema.

  • Column that has a configured generator is removed from the schema.

  • Column that has a configured generator changes data type.

  • Column that is assigned the NULL generator changes nullability.

  • Column that has an assigned generator becomes a foreign key. Foreign key columns must inherit the generator from the primary key.

Non-conflicting schema changes

Required license: Professional or Enterprise

Non-conflicting schema changes do not cause the data generation to fail. However, to prevent leakage of sensitive data, you should address these changes before you generate data.

Structural detects the following non-conflicting schema changes:

  • Column is added to the schema.

  • Table without generator configuration is removed from the schema.

  • Column without a configured generator is removed from the schema.

  • Column without a configured generator changes data type.

Schedule for schema change scans

When you navigate to a workspace in Structural, Structural always runs a scan to check for schema changes.

Structural can also run a periodic schema change detection scan in the background.

For databases other than Databricks, Snowflake on AWS, Snowflake on Azure, and MongoDB, Structural by default runs a background scan every two hours.

For Databricks, Snowflake on AWS, Snowflake on Azure, and MongoDB, Structural does not run any periodic scans. The data structure for these databases makes it expensive to run them. Instead, you can enable a daily schema change detection scan.

For information on how to configure whether and when Structural runs the periodic or daily detection scans, go to Configuring schema change detection.

Configuring schema change detection

  • TONIC_ENABLE_QUICK_PERIODIC_SCHEMA_CHANGE_SCANS - Boolean to indicate whether to enable the periodic background schema change scan. Default is true.

  • TONIC_PERIODIC_QUICK_SCHEMA_CHANGE_SCAN_INTERVAL_IN_MINUTES - If periodic background schema change detection is enabled, the number of minutes between scans. The default value is 120, which indicates to run the schema change detection every two hours.

For Databricks, Snowflake on AWS, Snowflake on Azure, and MongoDB, use the following environment settings to enable and configure the daily schema change detection scan.

  • TONIC_ENABLE_DAILY_EXPENSIVE_SCHEMA_CHANGE_SCANS - Boolean to indicate whether to enable the daily schema change detection scan. Default is false.

  • TONIC_DAILY_EXPENSIVE_SCHEMA_CHANGE_SCANS_HOUR - If the daily schema change detection is enabled, this sets the hour at which to run the scan. The value is an integer between 0 and 23, where 0 is midnight and 23 is 11:00 PM. For example, a value of 14 indicates to run the job at 2:00 PM every day. Default is 0.

Preventing data generation when there are schema changes

Conflicting schema issues always prevent data generation. By default, non-conflicting schema changes do not block data generation.

However, you can configure Structural to always prevent data generation whenever there are any unacknowledged or unresolved schema changes.

To block data generation for any schema changes, on the Edit Workspace page, under Source Settings, toggle the Block data generation if schema changes detected setting to the on position.

Viewing the unaddressed schema changes

Workspaces view provides a summary of the unaddressed schema changes for each workspace. Schema Changes view contains the complete list.

Schema change information on the Workspaces view

On Workspaces view, the Schema Changes column shows the number of conflicting and non-conflicting schema changes.

To display a more detailed summary of the schema change detection, hover over the column. The summary includes the timestamp of the last schema scan, and a link to Schema Changes view.

Displaying the Schema Changes view

To display Schema Changes view, either:

  • On the workspace management view, in the workspace navigation bar, click Schema Changes.

  • On Workspaces view, from the dropdown menu in the Name column, select Schema Changes.

Resolving conflicting schema issues

To resolve an issue, you must have permission to perform the associated action.

The Conflicting Schema Issues list contains the schema changes that make your current workspace configuration invalid and that you have not yet resolved.

An issue is resolved when either:

  • You resolve the issue from the Conflicting Schema Issues list.

  • For columns that have nullability or data type changes, you change the assigned generator in Privacy Hub, Database View, or Table View.

If there are any unresolved conflicting schema issues, then data generation is blocked. If there are no conflicting schema issues, then the Conflicting Schema Issues section is not displayed.

If there is a conflicting change for the removed table or column in the parent workspace configuration, then regardless of whether the configuration is inherited, you must resolve the change in the parent workspace before the change is resolved for the child workspace.

For changes to column nullability or data type, you resolve the change separately in the child and parent workspaces. Depending on the configuration, the conflict might only exist in one of the workspaces.

Information in the issues list

For each issue, the list includes:

  • Table name.

  • Column name, if the change affects a specific column.

  • Path, for JSON columns that use Document View.

  • Description of the schema change.

  • For changes to columns data type or nullability, a link to Database View. The link filters Database View to display only that column.

  • Resolve button or Select dropdown list. For changes to column data type or nullability, the Select dropdown list allows you to either resolve the issue or update the column configuration. For a child workspace, if the issue must be resolved in the parent workspace, the button is Go to Parent. If you do not have access to the parent workspace, then the button is disabled.

The list does not include changed or removed columns for which the assigned generator is Passthrough.

Resolving a single issue

For the following types of issues, you can only resolve the issue. Resolving the issues allows Structural to do the required cleanup to reflect the removal. For more information, go to How Structural resolves conflicting issues.

  • Removed table

  • Removed column

For these issues, to resolve the issue, click Resolve.

For a column that changed nullability or data type, you can use the Select dropdown list to either:

  • Resolve the issue. For more information, go to How Structural resolves conflicting issues.

  • Assign a different generator to the column and then resolve the issue.

For these issues:

  1. Click Select.

  2. To have Structural resolve the issue:

    1. Select Reset to Passthrough.

    2. On the confirmation dialog, click Resolve.

  3. To select a different generator for the column:

    1. Select Apply New Generator.

    2. On the generator configuration panel, select and configure the generator. For detailed configuration options for each generator, go to the Generator reference.

    3. When you change the generator configuration, the Mark Resolved button is enabled. To close the panel and also resolve the issue, click Mark Resolved.

For a child workspace, if the issue must also be resolved in the parent workspace, then the button changes to Go to Parent.

Resolving all of the issues

To resolve all of the issues:

  1. Click Resolve All Issues.

  2. On the confirmation dialog, click Resolve All.

For more information, go to How Structural resolves conflicting issues.

For a child workspace, for issues that must also be resolved in the parent workspace, the button changes to Go to Parent.

How Structural resolves conflicting issues

To resolve conflicting issues, other than for the columns that you assign a new generator to, Structural takes the following actions:

  • Removes the configuration for the affected table or column.

  • For a column that has a changed data type or nullability, Structural resets the generator to Passthrough.

  • Removes the links to the affected columns. The columns that were linked to the affected columns otherwise keep their current configuration.

Dismissing non-conflicting schema changes

Required license: Professional or Enterprise

The Notifications list contains schema changes that do not make the current configuration invalid. These changes are new tables and new columns.

If there are non-conflicting schema changes, then data generation is blocked only if you configured your workspace to block data generation for all unaddressed schema changes.

If there are no non-conflicting schema changes, then the Notifications list is not displayed.

Structural automatically dismisses a notification when:

  • You assign Truncate or Preserve Destination table mode to a new table.

  • You assign a generator other than Passthrough to a new column.

Dismissed notifications are removed from the list. Dismissing a notification does not change your workspace configuration.

Information in the notifications list

For each notification, the list includes:

  • Table name.

  • Column name, for new columns.

  • Description of the schema change.

  • A link to Database View. The link automatically filters Database View to only display the affected table or column.

  • Dismiss button or Select dropdown list. For new columns, the Select dropdown list allows you to either dismiss the notification or assign a generator to the column.

Dismissing an individual notification

For a new table, the only option is to dismiss the notification. To dismiss the notification, click Dismiss.

For a new column, you can use the Select dropdown list to either:

  • Dismiss the notification.

  • Assign a different generator to the column and then dismiss the notification.

For a new column:

  1. Click Select.

  2. To dismiss the notification, select Dismiss Notification.

  3. To assign a generator for the column:

    1. Select Apply New Generator.

    2. On the generator configuration panel, select and configure the generator. For detailed configuration options for each generator, go to the Generator reference.

    3. When you change the generator configuration, the Dismiss button is enabled. To close the panel and also dismiss the notification, click Dismiss.

Dismissing all of the notifications

To dismiss all of the notifications:

  1. Click Dismiss All Notifications.

  2. On the confirmation dialog, click Dismiss All.

Rerunning the sensitivity scan to check for new sensitive data

Whenever there are schema changes, especially new tables and columns, it is important to determine whether those new tables and columns contain sensitive data.

By default, Structural copies all rows from a table. The column generator is set to Passthrough, meaning that the source data is copied as is to the destination database.

Last updated 8 days ago

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Table is added to the schema. This includes new file groups that you add to a workspace.

For data connectors other than Databricks, Snowflake on AWS, Snowflake on Azure, and MongoDB, you use the following to configure the periodic schema change detection. You configure the settings in the web server container:

For , for removed tables and columns, when a child workspace overrides the parent workspace configuration for the table or column, you must resolve the change in the child workspace.

For Basic license users, if you know that there are non-conflicting changes, you can to get the protection status of the new columns.

Structural does not automatically dismiss non-conflicting schema changes in a , even if the parent workspace configuration is updated. You always dismiss the changes separately in the parent and child workspaces.

From Privacy Hub, you can . You can then use the updated results to guide the table and column configuration.

file connector
environment settings
parent and child workspaces
child workspace
run a new sensitivity scan
run a new sensitivity scan
Workspace setting to block data generation if there are schema changes
Schema changes overview on the Workspaces view
Conflicting Schema Issues list on the Schema Changes view
Confirmation dialog to resolve all issues
Notifications list on the Schema Changes view
Confirmation dialog to dismiss all notifications