> 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 by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` 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 what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. 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.