What does the append transformation do in data processing?

The append transformation is a crucial operation in data processing that combines the rows of two or more datasets into a single dataset. This transformation essentially stacks the datasets on top of one another, allowing for a unified set of data that can be analyzed collectively.

For example, if you have two datasets containing sales data from different regions, using the append transformation will create one larger dataset that encompasses all the sales data from both regions. This is particularly useful when dealing with similar datasets collected over different time periods or from varied sources, as it enables analysts to work with a comprehensive view of the data.

In contrast, other transformations mentioned in the choices serve different purposes. Removing duplicates focuses on ensuring that each entry in a dataset is unique, which does not align with the function of the append transformation. Creating new columns through merging datasets is more aligned with the join operation, which combines datasets based on a common key rather than stacking rows. Finally, converting data types is concerned with data compatibility but does not relate to the operation of appending datasets. Therefore, the append transformation's sole purpose of combining rows into a single dataset is accurately represented in the correct choice.