How to measure performance in HourGlass Model and Modified HourGlass Model
Hello All,
I'm trying to understand as to how the HourGlass Model (which says that the outline should be designed as Dimension tagged as Account, Dimension tagged as Time, Dense Dimensions from most dense to least dense, Sparse Dimensions from least sparse to most sparse) exactly works in terms of optimizing performance and aggregation.
Also I want to understand the working of the new Modified HourGlass Model on Stick (which says that the outline should be designed as Dimension tagged as Account, Dimension tagged as Time, Dense Dimensions from most dense to least dense, Aggregating Sparse Dimensions from least sparse to most sparse, Non Aggregating Sparse Dimensions).
Why are these approaches better and how do they work internally in the system?
How exactly does it pick up combinations during calculations and aggregations?
In some documents I learned that we should keep the Time dimension as the first dimension in the outline since it is dense and there are more chances of having similar kind of data values across the same fiscal year, due to which compression takes place efficiently. So if this is the case doesn’t it conflict with the HourGlass model and at such times which model to go with?
Thank You,
MM