Leverage dbt and its advanced scripting functionality to generate dynamic pivot tables that adapt to changing pivot values
When coping with tabular data, you frequently can have that data specified by a format that most accurately fits day-to-day operations. For instance, rows that represent purchase transactions for various customers.
A pivot table means that you can rotate rows into columns, making a more organized and digestible format for big datasets. You may as well aggregate data, corresponding to calculating sums, averages, or counts, and display these results as unique columns for every summarized category.
Let’s take a have a look at a visible that can help describe it. Here, we’re grouping by the user value, then taking the sum of the count of every transaction and creating a novel column for every product.
Most of us will approach this by writing SQL with case statements for every category. While this works for easy cases, if you happen to…
