Row Normaliser
Description
Row Normaliser converts de-normalized (pivoted) data back into a standard row-based format by transforming columns into rows. Use this step when your source data has values spread across multiple columns that should be in separate rows — for example, converting monthly sales columns (Jan, Feb, Mar) into individual rows with a month field and a value field. This is the reverse operation of Row Denormaliser and is essential for preparing data for analysis, reporting, or loading into normalized database tables.
Configurations
Following is an example where Row Normaliser step normalizes data back from pivoted tables as demonstrated below. Below is a sample table of product sales data:
| Month | Product | sales |
|---|---|---|
| 2003/01 | A | 0 |
| 2003/01 | B | 5 |
| 2003/01 | C | 17 |
| 2003/02 | A | 12 |
| 2003/02 | B | 7 |
| 2003/02 | C | 19 |
| ... | ... | ... |
| Field Name | Description |
|---|---|
| Step name | Name of the step as it appears in the workflow workspace. This name has to be unique in a single workflow. |
| Typefield | The name of the type field (product in the example above) |
| Fields table | A list of the fields you want to normalize; you must set the following properties for each selected field: - Fieldname: Name of the fields to normalize (Product A ? C in the example). - Type: Give a string to classify the field (A, B or C in our example). - New field: You can give one or more fields where the new value should transfer to (sales in our example). |
| Get Fields | Click to retrieve a list of all fields coming in on the stream(s). |