Creating Dynamic Pivots on Snowflake Tables with dbt | by Brian Roepke | Nov, 2024

Leverage dbt and its advanced scripting functionality to generate dynamic pivot tables that adapt to changing pivot values

When dealing with tabular data, you often will have that data laid out in a format that best suits day-to-day operations. For example, rows that represent purchase transactions for different customers.

A pivot table allows you to rotate rows into columns, creating a more organized and digestible format for large datasets. You can also aggregate data, such as calculating sums, averages, or counts, and display these results as unique columns for each summarized category.

Let’s take a look at a visual that will help describe it. Here, we’re grouping by the user value, then taking the sum of the count of each transaction and creating a unique column for each product.

Most of us will approach this by writing SQL with case statements for each category. While this works for simple cases, if you…