Building a Polished Cube October 25, 2008 Pam Shaw

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

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

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

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.

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.

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.

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

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, …

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?

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.

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.

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.

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

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

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

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)

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.

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.

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

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

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.

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

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

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

Resources Cube definition http://msdn.microsoft.com/en-us/library/ms175680(SQL.90).aspx

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)

Contact Info You can reach me at: PShaw1129@Live.com User Group Web Site TampaSQL.com