Introduction

The main purpose of Kettle transformations is to manipulate data in the form of a dataset—a task done by the steps of the transformation. When a transformation is launched, all its steps are started. During the execution, the steps work simultaneously reading rows from the incoming hops, processing them, and delivering them to the outgoing hops. When there are no more rows left, the execution of the transformation ends.

The dataset that flows from step to step is effectively a set of rows with the same metadata. This means that all rows have the same number of columns, and the columns in all rows have the same type and name.

Suppose that you have a single stream of data and that you apply the same transformations to all rows, that is, you have all steps connected in a row one after the other. In other words, you have the simplest of the transformations from the point of view of its structure. In this case, you don't have to worry much about the structure of your data stream, nor...