The Stack Rows transformation allows you to vertically combine data from two or more datasets, as long as the column names and types match. This is useful when you're working with partitioned or segmented data (e.g., monthly files, regional exports) and need to consolidate them into one dataset for analysis or reporting.
When to Use Stack Rows
Use this transformation when:
You have multiple datasets with identical structures (e.g., same columns and data types)
You want to append rows from one dataset to another
You’re combining monthly reports, region-based exports, or time-based slices of the same schema
Requirements
To use Stack Rows, all datasets must meet the following criteria:
Same number of columns
Matching column names (case-sensitive)
Compatible column types (e.g., string, integer, date)
If your datasets don’t align perfectly, you may need to first apply transformations like:
Rename Column
Reorder Columns
Change the data type in the data set builder
How to Apply Stack Rows
Start a Pipeline and load your base dataset
Click + Add Step
Choose Stack Rows from the transformation list
Select the additional dataset(s) you want to stack onto your base dataset
Click Apply
Example Use Case
You have three separate CSV files:
Q1_Sales.csvQ2_Sales.csvQ3_Sales.csv
Each contains the same columns: Region, Salesperson, Amount, Quarter.
Using Stack Rows, you can combine all three into a single dataset for year-to-date reporting—without manually merging files outside Nexadata.
Tip: Fixing Mismatched Data Types
If a dataset isn’t appearing in the dropdown list, it may have incompatible data types.
Use the Dataset Builder to change column types before applying Stack Rows:
Go to Datasets
Open the dataset you want to modify
Use the column editor to change data types (e.g., from string to integer)
Save and revalidate the dataset
Once aligned, the dataset will appear as an option when stacking rows.
Things to Keep in Mind
❗ Mismatched columns will cause errors or missing values.
✅ Always preview the datasets beforehand to confirm alignment.
🧰 Use Advanced Mode to inspect or adjust transformation steps if needed.