Aggregation, Pivot, Join, and Union

You will often encounter certain scenarios where the data might need to be adjusted to suit the visualization requirements. For example, if you are analyzing the monthly sales for your company, you don't need the data for every single day. In this case, you need to aggregate data to the monthly level. This also reduces the amount of data being used for analysis.

Another example, is when the data for all the past years is stored as standalone files, and the current year is stored as a separate file. All the files have a similar column structure. If you were to analyze all the data together, you may need to perform a union transformation to combine all these separate files into a single file.

Such data transformations can be done in Prep. You will now learn about how to do them.

Aggregations

Aggregations help to change the granularity of data. Granularity, in this context, means the level at which the data is available. For example...