> For the complete documentation index, see [llms.txt](https://docs.sdv.dev/rdt/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.sdv.dev/rdt/transformers-glossary/datetime.md). # Datetime Datetime columns indicate a point of time. This can be at any granularity: to the nearest day, minute, second or even nanosecond. Typically, the datetime will be represented as a string. ![For example, you might be storing the last day users have logged into your site.](https://2225246359-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FVGX92M819eIp0rMg5elc%2Fuploads%2FpQxH7ioA9OUNRuSyAfU4%2Frdt_transformers-glossary-datetime_June%2002%202025.png?alt=media\&token=99b331c4-2ce7-4f76-ae6e-b68426cdcb02) {% hint style="warning" %} Always double check to see if the datetime columns are being detected. A datetime column might be incorrectly detected as categorical if it's in a non-standard format. {% endhint %} ## General Datetime Transformers These transformers encode your datetime data as numerical values, ready for data science and machine learning.


UnixTimestampEncoder	Encode datetimes as numerical values based on the Unix timestamp.	UnixTimestampEncoder
OptimizedTimestamp Encoder	Encode datetimes as numerical values using the least common denominator.	OptimizedTimestampEncoder

## Differential Privacy Transformers These transformers use differential privacy techniques to add noise or reshape your column of datetime data. As a result, your column — and any statistics about it — can be shared with differential privacy guarantees. {% hint style="info" %} ❖ **SDV Enterprise Bundle**. This feature is available as part of the **Differential Privacy Bundle**, an optional add-on to SDV Enterprise. For more information, please visit the [Differential Privacy Bundle](https://docs.sdv.dev/sdv/reference/explore-sdv/differential-privacy-bundle) page. *Coming soon!* {% endhint %}


❖ DPTimestampLaplaceNoiser	Privatize the data by adding Laplacian noise	❖ DPTimestampLaplaceNoiser

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