One of the commonly used transformations in ETL is Table denormalization.

Denormalization is the reverse of normalization. Row-based data with key-value pairs is transposed into column-based data. This allows an OLAP model with one measure in each row (with the measure name as a key) to be arranged into a relational model in which the measures are held in various columns.

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In typical table denormalization, Measure fields are populated by the rows which we want to see in columns. Usually, it’s enough to do it one by one but sometimes it happens that there are 30 or 50 elements that needed to be transposed into the columns.

denormalization measures

For that reason, we will use a little trick that can be found in Advanced settings. In case you are not familiar with this type of transformation here is the link that can help.

Our file contains 3 columns and one of them contains data that should be denormalized.

Dynamic denormalization Jedox 1

Instead of using static measures, we will introduce the Dynamic Measure source. That will be nothing more than the isolated column from data fact. In our case, it is presented as a Date.

Dynamic denormalization Jedox 2

The output will look like this.

Dynamic denormalization Jedox 3

The full project can be downloaded from here.