Using tables

Overview

As the fundamental data-containing entity on Redivis, you will likely be interacting with tables a lot. While tables can just be downloaded, it will often be easier and more performant to work with tables directly on Redivis, particularly when the table is large.

Exploring tables

To develop a baseline understanding of a table and its contents, you will often start by viewing the table. You can browse its variables, read univariate summary statistics, scroll through cells, and execute one-off SQL queries to better understand a table's contents.

Querying in a transform

Tables can be referenced by any transform in a workflow. Transforms allow you to construct queries that filter, join, merge, and compute upon any table on Redivis, at scale. Every transform produces a single output table, which can subsequently be used for downstream analysis.

Loading and querying in a notebook

Tables can also be referenced by any notebook in a workflow. These notebooks allow you to execute Python, R, Stata, and SAS code within a flexible computational environment. The code that you write in a Redivis notebook is the same as what you would write from any external system referencing Redivis tables, ensuring portability of your analysis.

Importing into a dataset

Finally, tables can be imported into datasets! This can be helpful when you're using workflows to construct a "cleaned" version of a dataset, or when you want to store a final, permanent artifact of your analysis results.