> 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/sampling/export.md).

# Exporting Synthetic Data

Export your synthetic data back into a similar format as the original data or into a new format using the functions describe on this page.

## ❖ Export to a Database

{% hint style="info" %}
❖ **SDV Enterprise Bundle**. This feature is available as part of the **AI Connectors Bundle**, an optional add-on to SDV Enterprise. For more information, please visit the [AI Connectors Bundle](/sdv/explore/sdv-bundles/ai-connectors.md) page.
{% endhint %}

Write your synthetic data into a database to use for downstream applications. Many users choose to write to a database in a lower environment for testing and development. You can also append the synthetic data to an existing database.

We recommending using the same AI Connector you used for importing training data. This connector already knows your original database schema, so it will create the same schema when exporting synthetic data. Be sure to set your export config, so the connector knows where to write the synthetic data.

For more information, see [Connect Your Database](/sdv/integration/db.md) docs. 

```python
connector.set_export_config(...)
connector.export(data=synthetic_data, mode='write')
```

{% hint style="info" %}
**Exporting to a different type of database?** Create a new connector based on the type of database. Then, set its export config and complete the export.
{% endhint %}

## Write Locally

We recommend using the same data handler that you used for importing training data. This writes multiple files corresponding to each table you have synthesized. For more information, see the [Import Local Data](/sdv/integration/local.md) docs.

```python
connector.write(
  synthetic_data,
  folder_name='project/synthetic_data',
  to_csv_parameters={
      'encoding': 'latin-1',
      'index': False
  }
  mode='x')
)
```

{% hint style="info" %}
**Write to a different type of file?** Create a new handler based on the desired file format and then perform the write. 
{% endhint %}

{% hint style="info" %}
**Write other file formats directly from pandas.** The SDV uses the [pandas library](https://pandas.pydata.org/) for data manipulation and synthesizing. Pandas offers native support for many other types of files. For more information, see the [pandas reference](https://pandas.pydata.org/docs/reference/io.html).

If you have a multi-table dataset, be sure to iterate through all the tables of your multi-table dataset. Pandas typically writes one table at a time.

```python
guests_table = synthetic_data['guests']
guests_table.to_parquet('guests.parquet')

hotels_table = synthetic_data['hotels']
hotels_table.to_parquet('hotels.parquet')
```

{% endhint %}


---

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