Using an extended interval match to handle slowly changing dimensions

Sometimes, while developing the data model for a Business Intelligence application, you encounter dimensional values that tend to change over time. Such dimensions are known as slowly changing dimensions. For example, an employee joins a company at a Junior Executive level and stays in the same position for one year. After one year, the designation changes to Senior Executive and then changes to Project Manager after three years. The position field, in this case, will be treated as a Slowly Changing Dimension. Such Slowly Changing Dimensions can be represented in Qlik Sense, provided the historical data is stored at the source with a proper "Position Start Date" and "Position End Date." In order to match the discrete date values to the date intervals, we will make use of the intervalmatch function. At the same time, we will match the values of the primary key. This will help us to build an optimized data model and properly...