1. Module 7: Working with Measures and Measure Groups
Course 10778A
Module 7: Working with Measures and Measure Groups
Module 7
Working with Measures and Measure Groups

2. Module 7: Working with Measures and Measure Groups
Course 10778A
Module Overview
Module 7: Working with Measures and Measure Groups
Working with Measures
Working with Measure Groups

3. Lesson 1: Working with Measures
Course 10778A
Lesson 1: Working with Measures
Module 7: Working with Measures and Measure Groups
Introducing Measures
Measure Properties
Configuring How Measures Are Displayed
Aggregation Functions

4. Module 7: Working with Measures and Measure Groups
Course 10778A
Introducing Measures
Module 7: Working with Measures and Measure Groups
Measures:
Are values in a fact table
Are typically numeric and aggregated
Can be calculated from other measures

5. Module 7: Working with Measures and Measure Groups
Course 10778A
Measure Properties
Module 7: Working with Measures and Measure Groups
Measure properties include:
How measures are aggregated
The column in the data source view to which the measure is bound
The name that is displayed
The folder in which the measure will appear when users connect to the cube
The display format
Any MDX expressions that define the measure
The visibility of the measure

6. Configuring How Measures Are Displayed
Course 10778A
Configuring How Measures Are Displayed
Module 7: Working with Measures and Measure Groups
Source data type
Format
Display format value
Example output
Numeric
Named
General Number
Explain that the formatting is applied in clients that support it.
Fixed
User-defined
$#,#.00
$123,456,789.00
#,#0.0000
123,456,
Date/time
Named
Medium Date
31-Dec-06
Long Date
Sunday, December 31, 2006
User-defined
mm/dd/yyyy
12/31/2006
mmm-dd-yyyy
Dec
Boolean
Named
Yes/No
Yes
True/False
True

7. Aggregation Functions
Course 10778A
Aggregation Functions
Module 7: Working with Measures and Measure Groups
20,000
15,999
4,000
31,123
16,000
?,????
Aggregation functions can be:
Additive
Semiadditive
Nonadditive

8. Lesson 2: Working with Measure Groups
Course 10778A
Lesson 2: Working with Measure Groups
Module 7: Working with Measures and Measure Groups
Introducing Measure Groups
Measure Group Properties
Relationships Between Measure Groups and Dimensions
Aggregations
Configuring Measure Group Storage
Partitions

9. Introducing Measure Groups
Course 10778A
Introducing Measure Groups
Module 7: Working with Measures and Measure Groups
Measures groups define collections of related measures
By default, a measure group is created for each fact table
A measure group defines:
The dimensions the measures can be aggregated by
The granularity at which the measures are related to the dimensions
How measure aggregations should be stored

10. Measure Group Properties
Course 10778A
Measure Group Properties
Module 7: Working with Measures and Measure Groups
AggregationPrefix
DataAggregation
ErrorConfiguration
EstimatedRows and EstimatedSize ID
IgnoreUnrelatedDimensions
ProactiveCaching
ProcessingMode and Processing Priority
StorageLocation and StorageMode
Type

11. Relationships Between Measure Groups and Dimensions
Course 10778A
Relationships Between Measure Groups and Dimensions
Module 7: Working with Measures and Measure Groups
Regular: When the key column for a dimension is joined directly to the fact table
Reference: When the key column for a dimension is joined indirectly to the fact table
Fact: Standard dimensions that are constructed from attribute columns in fact tables instead of dimension tables
Many-to-many: When a single fact is joined to multiple dimension members

12. Module 7: Working with Measures and Measure Groups
Course 10778A
Demonstration: Defining Relationships Between Dimensions and Measure Groups
Module 7: Working with Measures and Measure Groups
In this demonstration, you will see how to:
Define a referenced relationship
Task 1: Define a referenced relationship
Ensure that the MIA-DC1 and MIA-SQLBI virtual machines are both running, and then log on to MIA- SQLBI as ADVENTUREWORKS\Student with the password Pa$$w0rd. Then in the D:\10778A\Demofiles\Mod07 folder, run Setup.cmd as Administrator.
In the D:\10778A\Demofiles\Mod07 folder, double-click Adventure Works OLAP.sln to open the solution in SQL Server Data Tools.
In Solution Explorer, right-click Adventure Works OLAP and click Deploy. If prompted, specify the user name ADVENTUREWORKS\Student and the password Pa$$w0rd.
In Solution Explorer, double-click Adventure Works Cube.cube, and on the Browser tab, expand Measures and Internet Sales and drag the Sales Amount measure to the Drag levels or measures here to add to the query area. Then expand the Geography dimension and drag Country-Region to the left of the Sales Amount value. Note that the values for each region are the same. The aggregation is incorrect.
In Solution Explorer, right-click Geography.dim, and then click View Designer. Note that this dimension is based on the Geography table and has a Country-Region attribute.
In Solution Explorer, right-click Customer.dim, and then click View Designer. Note that this dimension is based on the Customer table, which includes a GeographyKey attribute that relates it to the Geography table.
Click the tab for the Adventure Works Cube.cube, and on the Cube Structure tab, in the Data Source View pane, note that there is no direct relationship between the InternetSales fact table and the Geography dimension table.
On the Dimension Usage tab, click the intersection of the Internet Sales measure group and the Geography dimension, click the ellipses (…) button.
In the Select relationship type list, select Referenced.
In the Intermediate dimension list, select Customer.
In the Reference dimension attribute list, select Geography Key, and in the Intermediate dimension attribute list, select Geography Key.
Click OK.
Click the tab for the Adventure Works Cube.cube, and on the Browser tab, click the Reconnect button. Then in the data grid area, click the Click to execute the query link. Note that the values for each region are now correct.
On the Dimension Usage tab, in the Dimensions list, right-click Geography, and then click Rename.
Change the name of this cube dimension to Reseller Geography, and then press Enter to make this name change take effect.

13. Module 7: Working with Measures and Measure Groups
Course 10778A
Aggregations
Module 7: Working with Measures and Measure Groups
Aggregations:
Are precalculated summaries of leaf data
Give multidimensional cubes their performance benefits
Typically provide the best performance when only a proportion are precalculated

14. Configuring Measure Group Storage
Course 10778A
Configuring Measure Group Storage
Module 7: Working with Measures and Measure Groups
MOLAP
Discuss scenarios when you might use the different storage models. Also mention that the Tabular Data Model is an alternative and is covered in module 10.
ROLAP
HOLAP

15. Module 7: Working with Measures and Measure Groups
Course 10778A
Partitions
Module 7: Working with Measures and Measure Groups
SSAS Enterprise Edition uses partitions to manage and store data and aggregations for a measure group in a cube
When you incrementally update a partition, a temporary partition is created that has an identical structure to the source partition
You can partition a measure group horizontally or vertically

16. Module 7: Working with Measures and Measure Groups
Course 10778A
Lab Scenario
Module 7: Working with Measures and Measure Groups
Business users are performing analysis of Internet sales with the multidimensional cube you have created. However, they also need to analyze reseller sales. You have reseller sales data in the data warehouse on which the cube is based, so you plan to add a second measure group containing the reseller measures. Users have also requested that you remove some measures that they do not need to use when analyzing data, and ensure that the remaining measures are clearly named. Users specifically need to analyze reseller sales by product, so you must ensure that the cube supports the required relationship between the new reseller sales measure group and the Product dimension Finally, the data center administrator is concerned about the disk space used by the cube, but your users are concerned about performance when analyzing Internet sales. You must optimize the storage and aggregations of the cube to balance these concerns.
Students will perform the lab in the role of a BI professional in the Adventure Works Cycles company, and:
Add a measure group and modify measures
Define a relationships between the Reseller Sales measure group and the Product dimension
Configure measure group storage and aggregations
Point out that the instructions in the lab are deliberately designed to be high-level so that students need to think carefully about what they are trying to accomplish and work out how best to proceed for themselves. Encourage students to read the scenario information carefully and collaborate with each other to meet the scenario requirements. Remind students that if they find a particular task or exercise too challenging, they can find step-by-step instructions in the lab answer key.

17. Lab 7: Configuring Measures and Measure Groups
Course 10778A
Lab 7: Configuring Measures and Measure Groups
Module 7: Working with Measures and Measure Groups
Exercise 1: Configuring Measures
Exercise 2: Defining a Regular Relationship
Exercise 3: Configuring Measure Group Storage
In this lab, students will configure measures and measure groups in the Adventure Works OLAP cube.
Exercise 1
In this exercise, students will create and modify measure groups.
Exercise 2
In this exercise, students will create a regular relationship.
Exercise 3
In this exercise, students will configure the storage mode of a measure group and design aggregations for a measure group.
Logon information
Virtual machine
MIA-SQLBI
User name
ADVENTUREWORKS\Student
Password
Pa$$w0rd
Estimated time: 60 minutes

18. Module Review and Takeaways
Course 10778A
Module Review and Takeaways
Module 7: Working with Measures and Measure Groups
Give an example of an additive measure, a semiadditive measure, and a nonadditive measure.
What are the two ways in which you can partition a measure group?
How can you use aggregations to improve query response time?
Review Questions
Point the students to the appropriate section in the course so that they are able to answer the questions presented in this section. Some guidance for discussing the answers to the questions is included below.
Give an example of an additive measure, a semiadditive measure, and a nonadditive measure.
Additive: Sales Amount in a Sales measure group, which for example can be aggregated by Sum across all dimensions.
Semiadditive: Balance in an Accounts measure group, which can be aggregated by Sum across customers but by LastNonEmpty when aggregated by time.
Nonadditive: Profit Margin in a Sales measure group.
What are the two ways in which you can partition a measure group?
Horizontal and vertical.
How can you use aggregations to improve query response time?
Aggregations improve query response time by preparing the answers before the questions are asked.