> For the complete documentation index, see [llms.txt](https://docs.syntho.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.syntho.ai/overview/get-started/syntho-bootcamp/5.-generators/mock.md). # Mock [Mock generators](/configure-a-data-generation-job/configure-column-settings/mockers.md) are used to generate entirely new, random values for a column. They are ideal when privacy is the top priority, and there’s no need to maintain a link to the original data values. #### When to use * To fill columns that contain directly identifiable information (PII) * To populate empty columns * When format matters, but data relationships do not #### When not to use * When relationships with original data must be preserved * For key columns (e.g., primary or foreign keys) * When data correlations or dependencies matter * When maintaining statistical properties is important #### Available mock generators Mock are grouped by data type: * [Text](https://app.gitbook.com/s/SUjgQeXGZtfM3xg5Rj73/configure-a-data-generation-job/configure-column-settings/mockers/text) * Numeric ([integer ](https://app.gitbook.com/s/SUjgQeXGZtfM3xg5Rj73/configure-a-data-generation-job/configure-column-settings/mockers/numeric-integer)and [decimal](https://app.gitbook.com/s/SUjgQeXGZtfM3xg5Rj73/configure-a-data-generation-job/configure-column-settings/mockers/numeric-decimal)) * [Datetime](https://app.gitbook.com/s/SUjgQeXGZtfM3xg5Rj73/configure-a-data-generation-job/configure-column-settings/mockers/datetime) * [Other](https://app.gitbook.com/s/SUjgQeXGZtfM3xg5Rj73/configure-a-data-generation-job/configure-column-settings/mockers/other) #### Interactive guide: How to apply a mock generator Follow the interactive guide below to apply a mock generator. {% embed url="" %} * **Constraints on Key Columns:** Mock generators cannot be applied to primary key or foreign key columns. * **Column-by-Column Operation:** Mock generatorsfunction on individual columns. At this point, they can't be used to preserve logical relationships across multiple columns. * **Automatically Cutoff Values:** The Syntho platform automatically cuts off generated mock text values based on the data type's supported length. For example, a mock generator applied on an NVARCHAR(5) column, will cutoff all values beyond the first 5 characters of the text. * **Automatically Clip Values:** The Syntho platform automatically clips numerical values that exceed the maximum or minimum size, to the largest or smallest value supported by the data type, respectively. * **No Link with Original Records:** Mock generators do not link back to the original data records, enhancing privacy but potentially reducing the usefulness of the data. If you want to retain the link with the original values, you can enable the [**Consistent Mapping**](#mocker-parameters) feature. * **Database Type Compatibility:** The return type of a mock generator indicates its compatibility with specific database data types. For example, a mock generator with a text return type is compatible with database types like **(N)VARCHAR** or **TEXT**, but not with a database type **NUMERIC (INTEGER)**. It is important to this into account when applying mock generators on your columns to prevent your data generation job to fail. Being aware of these limitations and considerations will help you effectively use mock generators while understanding their constraints. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.syntho.ai/overview/get-started/syntho-bootcamp/5.-generators/mock.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.