1. Building a Polished Cube
October 25, 2008
Pam Shaw

2. About the Speaker Independent consultant at CSC Consulting
President of Tampa SQL User Group
TampaSQL.com
We meet 6:30 on 3rd Tuesday of the
Franklin Templeton Building on Feathersound
Next meeting: 11/18/2008

3. Agenda What is a Cube? Steps to building a successful cube Analysis
Data Mart
Cube Development

4. Spreadsheet on Steroids
What is a Cube?
Per Microsoft:
A cube is a set of related measures and dimensions that is used to analyze data. Or
Spreadsheet on Steroids

5. Measures
A measure is a fact, which is a transactional value or measurement that a user may want to aggregate. Measures are sourced from columns in one or more source tables, and are grouped into measure groups.

6. Dimensions
A dimension is a group of attributes that represent an area of interest related to the measures in the cube, and which are used to analyze the measures in the cube. Attributes are sourced from columns in one or more source tables. The attributes within each dimension can be organized into hierarchies to provide paths for analysis.

7. Dimesions (cont.)
For example, a Customer dimension might include the attributes Customer Name, Customer Gender, and Customer City, which would enable measures in the cube to be analyzed by Customer Name, Customer Gender, and Customer City.

8. Agenda What is a Cube? Steps to building a successful cube Analysis
Data Mart
Cube Development

9. Analysis Who do we involve? Who leads this phase? Tools Power Users
Reporting Specialists
End Users
Who leads this phase?
Business Analyst
Data Analyst
Tools
Excel, Word, Vision, …

10. Analysis What is the nature of the data we are working with ?
What is the data we measure success by ?
Are we capturing the data we need?

11. What is the nature of the data we are working with ?
Is it transactional or demographic?
Transactional data has a natural ‘history’ built in.
Demographic data must be captured on a regular basis building history a little at a time. Note that if lost – you loose the history.

12. What is the data we measure success by?
Sometimes referred to as KPIs – Key Performance Indicators; these end up being the backbone of dash boards.
We can identify these by reviewing data requested on Month end, Quarter end and Year end reports.
Don’t forget to review special data requests that have been made over time.

13. Are we capturing the data we need?
Is it in the current system?
Where is it located?
If the data is not currently in the system – but critical to our success – we need to determine where we are getting that data from today and determine the best means of assuring it is available to the system when needed.

14. Agenda What is a Cube? Steps to building a successful cube Analysis
Data Mart
Cube Development

15. Data Mart Who leads this phase? Tools ETL Developer DB Developer
SSMS – T-SQL
SQL Server Management Studio
BIDS – SSIS
Business Intelligence Development Studio

16. Data Mart
This is where the actual technical work begins. This is where we start to bring the data into a central location to start ‘analyzing’ it.
Source data
Transient staging data
Persisted staging data
Fact Tables
Dimension Tables

17. Fact Tables This data is almost completely numeric / integer.
It contains links to the dimensions.
Examples of the ‘measures’ are:
Counts of events
Indicators (stored as 1 or 0)
Quantities
Money
Volumes
(anything numeric that may point to how the business is operating)

18. Fact Tables
Fact tables should be ‘lean and mean’. Only add fields that will be used because the bulk of the processing in a cube is aggregating numbers. You don’t want to spend time aggregating something that will never be used.

19. Dimension Tables
These are the attributes that describe groups within the data. Some of these attributes may be hierarchical- others not. Key point is they describe the data.
These are where the key fields in the fact table point.
These hold the labels presented for slicing and dicing.
Dimensions can be more robust and serve as the source of data for reporting data that is never displayed in the cube itself.

20. Agenda What is a Cube? Steps to building a successful cube Analysis
Data Mart
Cube Development

21. Data Mart Who leads this phase? Tools BI Developer DB Developer
SSMS – Analysis Databases
SQL Server Management Studio
BIDS – SSAS
Business Intelligence Development Studio

22. Cube Development
Everything so far has been ground work – critical ground work.
Development of the cube is performed with BIDS using SSAS. The person working on this phase of the cube must understand what the end user needs to see. They will need to be intimate with the data mart.

23. Steps to building a Cube
Create a Project
Add a Data Source
Add a Data Source View
Add Dimensions
Time dimension is special
Hierarchies
Hide any data not useful to user
Add Cube

24. Steps to keep it clean
Delete any measures that are not going to be used – usually these are foreign keys
Hide any measure that is an intermediate step to a value presented to the user
Make sure that calculated measures are attached to the proper measure group

25. Additional Measure groups
Utilize named queries in the Data Source View to create new measure groups
Be sure to map new measure group to dimensions via the Dimension Usage tab

26. Resources
Cube definition

27. Reference Books
The MS Data Warehouse Toolkit, Joy Mundy and Warren Thornthwaite with Ralph Kimbal (Wiley)
MDX Solutions, 2nd Edition, George Spofford.. (Wiley)
MS SQL Server 2005 Analysis Services, Reed Jacobson, Stacia Misner (MS Press)
Delivering Business Intelligence with MS SQL Server 2005, Brian Larsen (Osborne)

28. Contact Info
You can reach me at:
User Group Web Site
TampaSQL.com