SSAS - tabular | Changing query context exercise | Rank habitats by number of sales using the RANKX function

This exercise is provided to allow potential course delegates to choose the correct Wise Owl Microsoft training course, and may not be reproduced in whole or in part in any format without the prior written consent of Wise Owl.

Software ==> SSAS - tabular  (30 exercises)
Version ==> SSAS 2012 and later
Topic ==> Changing query context  (2 exercises)
Level ==> Average difficulty
Subject ==> SSAS training
Before you can do this exercise, you'll need to download and unzip this file (if you have any problems doing this, click here for help). Once you've done this:
  1. Go into SQL Server Management Studio;
  2. Open the SQL file you've just unzipped (you can press CTRL + O to do this); then
  3. Execute this script.

This will generate the database that you'll need to use in order to do this exercise (note that the database and script are only to be used for exercises published on this website, and may not be reused or distributed in any form without the prior written permission of Wise Owl).

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If you haven't already done so, create a new project called BaseModel, and import the following tables: tblCentre, tblCentreType, tblEnvironment, tblFamily, tblHabitat, tblProduct, tblPurchase, tblRegion and tblTown

Create a measure which ranks regions according to the count of the number of purchases.  The syntax of the RANKX function is shown below:

The RANKX function

Only the first two arguments are compulsory.

To start with, create a measure in the purchases table giving the number of purchases:

Counting purchases

What this measure should show in your model.


Now create a measure in the Habitat table to rank habitats according to the number of purchases, and use this to create this pivot table:

Ranking habitats

Grasslands is always the habitat with the most purchases, regardless of centre type, and Urban the one with the fewest purchases.

The lack of variety between shopping centre types might be almost enough to make you suspect that the data was randomly generated, and not genuine. 

Save this workbook as Suspiciously similar data, then close it down.

You can unzip this file to see the answers to this exercise, although please remember this is for your personal use only.
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