> For the complete documentation index, see [llms.txt](https://docs.sdv.dev/sdv/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/sdv/explore/sdv-bundles/xsynthesizers.md).

# ❖ XSynthesizers

Go the eXtra mile with your synthesis. Use enhanced synthesizers and transformers for **improved synthetic data quality and performance**.

{% hint style="info" icon="user" %}
Bundles are available to licensed users. Sign up now to get your license key. You can then complete your installation on-prem and begin using the features.

<p align="center"><a href="https://portal.datacebo.com/signup" class="button primary">Get Started</a><a href="https://datacebo.com/pricing/" class="button secondary">View Pricing</a></p>
{% endhint %}

## Included Features

This bundle includes versions of synthesizers and transformers that have been expanded with additional features.

**Enhanced synthesizers for higher quality synthetic data.** Use this to model your real data and create synthetic data.

* [**XGCSynthesizer**](/sdv/modeling/single-table-synthesizers/xgcsynthesizer.md) is expanded version of the [GaussianCopulaSynthesizer](/sdv/modeling/single-table-synthesizers/gaussiancopulasynthesizer.md) that includes modeling support for 100+ additional column shapes. Use this single-table synthesizer in combination with multi-table synthesizers such as [HSASynthesizer](/sdv/modeling/multi-table-synthesizers/hsasynthesizer.md).
* [**SegmentSynthesizer**](/sdv/modeling/single-table-synthesizers/segmentsynthesizer.md) is a specialty synthesizer that you can use for a table of highly segmented data.
* [**BootstrapSynthesizer**](/sdv/modeling/single-table-synthesizers/bootstrapsynthesizer.md), designed specially to learn from data with only a few rows, or "short and wide" data containing more columns than rows.

**Enhanced transformers for more accurate data preprocessing.** Use these as a preprocessing step with any SDV synthesizer.

* [**XGaussianNormalizer**](https://docs.sdv.dev/rdt/transformers-glossary/numerical/xgaussiannormalizer) is an expanded version of the [GaussianNormalizer](https://docs.sdv.dev/rdt/transformers-glossary/numerical/gaussiannormalizer) transformer. This also includes modeling support for 100+ additional column shapes. Use this transformer with any of the SDV synthesizers.
* [**ECDFNormalizer**](https://docs.sdv.dev/rdt/transformers-glossary/numerical/ecdfnormalizer) provides another way to normalize your data by direct, empirical computation. With this transformer, there is no need to choose a predefined column shape. Use this transformer with any of the SDV synthesizers.

## Installation

Use your SDV Enterprise credentials to install SDV Enterprise and all bundles that you have access to.

```bash
% pip install sdv-installer --upgrade
% sdv-installer install --upgrade
```

```
Username: <email>
License Key: ********************************

Installing SDV Enterprise:
sdv-enterprise (version 0.30.0) - Installed!

Installing Bundles:
bundle-cag - Installed!
bundle-xsynthesizers - Installed!

Success! All packages have been installed. You are ready to use SDV Enterprise.
```

{% hint style="info" %}
For more information about installing SDV Enterprise features and bundles, see our [**Enterprise Installation Guide**](https://docs.sdv.dev/sdv-enterprise/sdv-enterprise/installation).
{% endhint %}


---

# 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.sdv.dev/sdv/explore/sdv-bundles/xsynthesizers.md?ask=<question>&goal=<user_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.