Upload tabular data as tables

Overview

Redivis offers extensive tools for previewing tabular data and transforming it in projects, but the data needs to be uploaded correctly in a tabular format for researchers to utilize these tools.

This guide assumes you have already started by Creating a dataset.

1. Locate the data you want to upload

You can upload data directly from your computer, or import from a linked account.

If importing, you'll want to get the relevant external account configured to your Redivis account before getting started.

The import tools allow for multiple uploads, so no need to combine files together before importing them, but it's helpful to have them all in the same place.

2. Create tables

On your newly created dataset, the first step to uploading data is to create one or more tables that the data will be uploaded to.

The data files you currently have may or may not be how you want to store them on this dataset, so it's important to think about your data's structure before getting started.

For example, if you have multiple files that all follow the same schema, we strongly recommend uploading them as one table (for example, if you have a separate table for each year, or a separate table for each state, but the structure of each is the same). In the example of one file per state, this would allow researchers to query across all states, skipping the first step of doing up to 50 joins. Additionally, you generally shouldn't split out tables for performance reasons — even when querying billions of records, Redivis will execute in seconds.

When uploading files to tables, remember that every row in a table should represent the same "thing", or entity; we wouldn't want to combine county-level and state-level observations in one table.

If you haven't already, we very strongly recommend experimenting with the reshaping and analytic tools in a project which researchers will use to work with your dataset. Knowing how they will work with it might inform how you structure it during this setup process, and can save time for everyone. You can even add your unreleased dataset to a project for testing — click on "View dataset page" from the dataset overview, and then add the unreleased dataset to your project.

When you're ready click the Create new table button on the Tables tab of the dataset page and name your table to get started.

3. Upload tabular file(s) to create a table

To get started uploading, choose the data source. By default this is your computer, but you can choose any option from the dropdown menu.

Next, choose the file(s) or enter the paths of the file(s) you want to import.

If you select multiple files here, they will be automatically appended in this single table on upload based on common variable names. If a variable is missing in some of the files, that's ok, it will just be recorded as null for all records in that file.

For a full list of supported file types, as well as advanced functionality (such as wildcard imports) and error handling techniques, consult the Uploading data reference.

Once your files are selected, click the Import button. If the files are coming from your computer, you might need to wait until they are finished uploading to the browser before they can be imported into Redivis.

Learn more in the Uploading data reference section.

4. Verify uploads

As you upload files, you will see an overview of any files' progress and can click to view each file's data and additional information.

Once all uploads have completed, you can inspect the table (representing the concatenation of all of your uploads). Make sure to check the summary statistics and other analytical information to validate that the data are as you expected.

If you have more files to upload you can click the Manage imports button on the right side of the table at any time (up until releasing this version of the dataset).

Next steps

Continue uploading your dataset

Great metadata makes your dataset useable. Complete your metadata, along with configuring access, creating a sample, and releasing this version.

Learn more in the Create & manage datasets guide.