Computed Columns
Last updated
Last updated
Datasets support the ability to define no-code calculations and aggregation from other datasets as new calculated columns without making changes in your warehouse yet still being fully governed, tracked, and observed by your data stack.
These columns can be then used as any other column in your dataset, as a sync attribute or during segmentation building.
You can create computed columns via the "New Computed Column" button in the Properties page of any of your datasets.
Computed Columns support following operations: Lookup Columns, Rollup Columns, and Equation Columns.
Lookup Columns allow you to pull in a value into your dataset from any other dataset related through a one-to-one or many-to-one mapping.
For example:
User team name
User organization sign up date
Rollup Columns allow you to create an aggregate value over your datasets relationships to help you obtain insights into your data. You are also able to filter down the related datasets over which you want to aggregate.
For example:
Number of transactions performed last 30 days
Number of payment failures last 90 days
Number of current active admin users
Most frequent purchase SKU
Average order value in the last 30 days
Rollups require you to define three properties: the related dataset which to aggregate/join to, the column from this dataset to use, and the aggregation method to apply to the related values. You are also able to use our rich filtering editor to narrow down the target rows. The current supported aggregation methods are listed below. We will continue to expand this list to allow for richer operations.
number
most frequent, count, sum, average
any other type
most frequent
Equation Columns allow you to do quick calculations with your dataset columns in a no code environment.
For example:
Weekly growth rates of feature usage
Difference month over month of documents shared
number
difference, percentage change
Refer to the dedicated documentation for GPT Columns to learn about defining these AI-powered columns for your datasets.
