Which transformation type is suitable for creating a new field while performing non-aggregate calculations?

The suitable transformation type for creating a new field while performing non-aggregate calculations is the Expression transformation. This transformation allows you to define and compute new fields using expressions and formulas based on existing data fields.

In the Expression transformation, you can perform various operations, such as concatenating strings, performing mathematical calculations, or converting data types. It provides the flexibility to create derived fields based on complex logic without altering the original data structure. This is specifically beneficial when there is a need to derive new values that are calculated but do not depend on aggregating multiple rows of data, which aligns perfectly with non-aggregate calculations.

The other options offer different functionalities that do not specifically cater to the creation of new fields through computed expressions. For example, the Filter transformation is used to specify which rows of data should be processed based on conditional logic, but it does not create new fields. The Joiner transformation is meant for combining data from multiple sources based on common keys, rather than creating new computed fields. The Normalizer transformation is specialized for converting data from a denormalized format to a normalized one, which involves restructuring data rather than creating new fields through calculations. Thus, the Expression transformation is the correct choice for the scenario presented.