Can 2 Cubes in a Data Warehouse be directly compared against each other?

Question

Is there a way to compare all information (aggregates, down to the detail level) between two OLAP cubes? For example, say I wanted to compare one cube created to work with sql server 2000 to that same cube, but migrated to run on sql server 2005/2008 - technically they should both return the same information for all dimension / measure combinations but I need a way to verify.

I am definitely NOT a developer, but I do have access to enterprise manager, and potentially SAS tools etc. and I know a bit of SQL but not much else. I know that you can compare two dimensional (i.e. tables) data sets with sql queries, and also with SAS - but I have never heard of a way to compare three dimensional cubes.

Am I out of luck on this one? The last thing that I want to have to do is view both cubes and compare all possible results side by side via excel or something, I hope that it can be automated somehow.

Answer 1

Comparing cubes means doing enough "slice-and-dice" queries to prove that you've queried all of the facts.

You can, simply, get a sum and count of the various fact and dimension tables. If those are the same, odds are good that any particular query will be the same between the two.

Without details on the dimensions and facts in question, it's hard to make a more specific recommendation.

However, consider that you can easily compute a set of subtotals for each dimension of the cube. If the dimensions are the same number of rows, the results will be the same number of rows. If the grand total is the same, then all that's left is row-by-row comparison of the subtotals.

If you do this once for each dimension, you should have some confidence that they're the same. Or, you'll find a difference that you can explore with more detailed queries.

Answer 2

The best approach is to compare the cube data by interchanging the rows and columns and verifying if all the counts and totals match properly.

For example, if you are having year-wise totals for a particular location, it would be a good approach to interchange the values between locations and the months and verifying whether they match properly.