Map sets of input columns into new output columns. This effectively performs an UNPIVOT on the data.
This component reshapes data by outputting multiple rows for each individual input row. Each set of input columns is mapped to an output column. The output rows are labelled to determine which column the value originated from.
Name = string
A human-readable name for the component.
Ordinary Columns = dual listbox
Choose the ordinary columns, those that are not going to be transposed but are still required in the output. These are effectively a set of grouping columns that are passed to the output unchanged.
Row Label Name = string
Provide the name of a new column here. It will contain constants you enter into the Column to Row Mapping, which identify the original column that the new row originated from.
Output Columns = column editor
- Name: A new column name to hold the output of multiple input columns.
- Type: Specify the data type for the column. Should be compatible with all input columns that will be mapped into this column. This is used to validate that the input columns all conform to the type of the output column.
Column to Row Mapping = editor
- Row Label Name: This editor column will actually appear as the label provided in Row Label Name. Enter an identifier to specify what the rows represent.
- Output Column-1: Each defined output column will appear as a column in this mapping. Add a row to this grid for each input column you want to map into an output column.
- Output Column-n: As above, if you are mapping multiple sets of input columns. When you map data into multiple output columns, there should be a set of similar input columns for each output column. For example, you may have a set of input columns for each quarterly revenue amount, and another set of input columns for quarterly profits.
|Snowflake||Databricks (preview)||Amazon Redshift (preview)|