Join (Merge columns, One-to-One demo template)

The Join building block merges columns.

For each row in the Primary dataset, it searches for a matching row in the Secondary dataset.

Join vs Lookup

Join functions identically to the Lookup block when the number of rows in the Primary dataset is the same as in the Secondary dataset. This is referred to as Join One-to-One, meaning one row in the Primary dataset corresponds to one row in the Secondary dataset.

If the number of rows differs (One-to-Many), the behavior of Join diverges from that of Lookup. We will address this case in a separate article.

We recommend using Lookup for most cases, as it is more straightforward and has fewer surprises.

Join Demo

Here we have two tables: rev2 and cost2. Our goal is to merge them into a single table that includes columns from both tables.

Below is a workflow that loads the source tables and creates a merged table.

You can view the result by clicking the "View" button in block #3.

This template demonstrates how to merge columns from two datasets that have the same number of rows. You can replace the Pull blocks with your own data sources.