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On this page
  • Overview
  • 1. Check export restrictions
  • 2. Prepare your data
  • 3. Export data
  • Next steps

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  1. Guides
  2. Export & publish your work

Export to other environments

Last updated 5 months ago

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Overview

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.

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.

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.

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.

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.

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.

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.

Integrations: Google Cloud Storage

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

Integrations: Google BigQuery

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

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.

Learn more in the guide.

Learn more in the guide.

Learn more in the reference section.

Learn more in the reference section.

Learn more in the reference section.

Learn more in the reference section.

Work with data in a workflow
Reshape tables in transform
Downloads
Redivis-python library
Redivis-r library
Google Looker Studio
Google Cloud Storage
Google BigQuery