# Export to other environments

Redivis workflows contain powerful tools to reshape and analyze data but if you prefer to export data into a different workflow you can easily do so. Redivis systems use open source tools and common formats to make the transition as easy as possible.

## 1. Check export restrictions

Some datasets on Redivis have export restrictions which prohibit or limit removing data from the system. 

You can check this by going to the dataset page and clicking the top right button **Manage access**, or by right-clicking on a dataset in a workflow and selecting the **View access** option. 

![](/files/4Suzp1O2XbFg1N0rSTJf)

The bottom section of this access modal defines any export restrictions in place. It might not have any restrictions, might be completely restricted, or might have some limited options for export. 

If there are limited options for export, this section will detail the available locations and any restrictions on using those (such as the size of the table being exported). These restrictions are enforced automatically when you try to take actions within the system.

If there are no options for export, you still have the option to work with your data in a Redivis workflow, where you can reshape and clean your data using transforms, and then analyze it in notebooks. 

*Learn more in the* [*Work with data in a workflow*](/guides/analyze-data-in-a-workflow.md) *guide.*

## 2. Prepare your data

If you would like to export the entire table, you can skip this step. 

Otherwise, we recommend that you use transforms to cut your data down to a smaller size and reshape it into the table format you need for analysis before exporting it. Especially for large datasets, Redivis tools are created specifically for these purposes and work seamlessly in the browser with no additional setup.

*Learn more in the* [*Reshape tables in transform*](/guides/analyze-data-in-a-workflow/reshape-data-in-transforms.md) *guide.*

## 3. Export data

You can open the Export modal to initiate an export from any table. You can do this on the dataset page by right clicking a table and selecting the **Export** menu option, or by right clicking on any table in a workflow and selecting the same option. 

Here you can see all the options available for your table to export. 

![](/files/9Qe68xF5Jxs8skLk73RD)

If your table has export restrictions set by the data owner, options will be disabled here. In the case where your table does not meet export requirements but the data owner allows exception requests, you will see the option to do so here.

**Download**

The first tab of this modal gives you different options for formats to download your data. Most common data formats are supported, including `csv`, `json`, `avro`, `parquet`, `SAS`, `Stata`, and `SPSS`. Select the format you'd like and click the **Download** button to start downloading the file.

*Learn more in the* [*Downloads*](/reference/tables/exporting-tables/download.md) reference section*.*

**Programmatic reference**

You can reference this file from your computer or another computational environment using the Redivis Python and R libraries. This modal gives specific information on how to reference this dataset, and you can reference our docs for other options. 

[Redivis-python library](/api/client-libraries/redivis-python.md)

[Redivis-r library](/api/client-libraries/redivis-r/reference/redivis.md)

**Integrations: Google Looker Studio**

This is a free Google dashboard visualization program you can directly link your table to. Use their point and click interface to build common visuals that will update as the underlying table updates. 

*Learn more in the* [*Google Looker Studio*](/reference/tables/exporting-tables/google-data-studio.md) reference section*.*

**Integrations: Google Cloud Storage**

You can export your table directly into a Google Cloud Storage bucket that you have access to.

*Learn more in the* [*Google Cloud Storage*](/reference/tables/exporting-tables/google-cloud-storage.md) reference section*.*

**Integrations: Google BigQuery**

You can export your table directly into a Google BigQuery project that you have access to. 

*Learn more in the* [*Google BigQuery*](/reference/tables/exporting-tables/google-bigquery.md) reference section*.*

## Next steps

#### Cite datasets in your publications

If the work you're doing leads to a publication, make sure to reference the dataset pages from datasets you've used for information from the data administrators on how to correctly cite it.


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