In most Kettle datasets, all rows share a common meaning; they represent the same kind of entity, for example:
In a dataset with sold items, each row has data about one item
In a dataset with the mean temperature for a range of days in five different regions, each row has the mean temperature for a different day in one of those regions
In a dataset with a list of people ordered by age range (0-10, 11-20, 20-40, and so on), each row has data about one person
Sometimes, there is a need of interspersing new rows between your current rows. Taking the previous examples, imagine the following situations:
In the sold items dataset, every 10 items, you have to insert a row with the running quantity of items and running sold price from the first line until that line.
In the temperature's dataset, you have to order the data by region and the last row for each region has to have the average temperature for that region.
In the people's dataset, for each age range...