Create a test database
- Navigate to Test Data → Structural in the left sidebar.
- Click New Database.
- Fill in the form:
- Database Name — A name for the synthetic database.
- Select Datasource — The source connection to replicate from.
- Select Datasets — Choose which tables to include. Use Select All or search to filter.
- Click Submit.
Database View
The Database View is where you configure which generators to use for each column. Left sidebar lists all tables from the selected datasource. Each table shows the column count. Filter by sensitivity using the At Risk, Protected, and Not Sensitive buttons. Column table shows the columns for the selected table:| Column | Description |
|---|---|
| Column Name | Name of the column, with source dataset shown for copied columns. |
| Status | Sensitivity classification — At Risk, Protected, or Not Sensitive. |
| Generator | The generator assigned to this column. |
| Unique | Whether generated values are enforced as unique. |
| Actions | Edit or delete the column configuration. |
Configure a column
Click the edit icon on any column to open the column editor:| Field | Description |
|---|---|
| Column Name | Name of the column. |
| Generator | The data generator to use. Search by name or filter by category. Each generator shows a description and example output. |
| Hide Column | If enabled, this column is excluded from the generated output. |
| Null Percentage | Percentage of rows that will contain a null value (0–100). |
| Format | Optional format pattern for the generated value. |
| Mask | Optional mask pattern applied to the value. |
Generator types
| Generator | Description |
|---|---|
| Standard generators | Email, phone, name, address, and more. See Test Data Generators for the full list. |
| value_of_column | Copies values from a specific column in the source datasource. Select the dataset and column. |
| custom_function | An AI-generated function based on the column name. |
| similar_values | Generates values similar to a list of examples you provide. |
| category_value | Generates values from a selected category. |
| value_of_virtual_table | Draws values from a pre-built virtual table. |
Row count
Click the edit icon next to the row count in the column table header to set how many rows to generate for each table.Add a column
Click Add Column to add a new column to a table beyond what was imported from the source.Privacy Hub
The Privacy Hub gives you a sensitivity analysis of all tables and columns imported from the source datasource. Use it to identify which columns contain sensitive data before configuring generators.Table View
The Table View previews the generated data before you export it. Select a table from the left sidebar to see a sample of the generated rows. From here you can:- Download Data — Save the generated dataset as a file.
- Export YAML — Export the generator configuration for reuse or version control.
- Regenerate Data — Trigger a new generation run with the current configuration.
Foreign Keys
Define relationships between tables to ensure generated data maintains referential integrity. Click New FK to create a relationship:- Foreign Key — Select the table and column that holds the foreign key.
- Primary Key — Select the table and column it references.
Data Targets
Data Targets define where the generated data is delivered — for example, an API endpoint or webhook.| Field | Description |
|---|---|
| Name | A label for this target. |
| Request Type | HTTP method — GET, POST, PUT, or DELETE. |
| Link | The endpoint URL. |
| Headers | Key-value pairs added to the request. |
| Request Body | JSON payload sent with the request. |
| Target Type | The type of destination. |