1. Data Warehouse to BI 1

2. Agenda  Review  Preparing the DW for Analysis  Microsoft BI Platform Overview  Building a Cube in SSAS 2

3. DW Development Approach: Kimball  Methodology  DW Project Lifecycle  Business requirements  Business Requirements Documentation  Bus Matrix  Design, build and deliver in increments  DW Architecture  DW Design  ETL system  Cube, Reports, query tools, … 3

4. Data Warehouse Project Lifecycle 4 Source: Mundy, Thornthwaite, and Kimball (2006). The Microsoft Data Warehouse Toolkit, Wiley Publishing Inc., Indianapolis, IN.

5. BI Application Specification, Development  Specification  Standard Reports  Layout-led discovery  Enrollments by course and timeframe  Average GPA by course and timeframe  Analytic Applications  Data and Model-led discovery  Year-to-year changes in average GPAs by college  Students most likely to have good overall GPAs  Development  (SSAS, Excel) Multidimensional Cube, Data-led discovery  (SSRS, Excel) Standard Reports  (SSAS, Excel) Mining Model-led discovery 5

6. Displaying the Cube  Cube browser—resembles spreadsheet 6

7. Displaying the Cube, cont…  Pivot table - a multidimensional spreadsheet

8. OLAP Models ModelDescriptionComparison MOLAPData and aggregates in multidimensional database Best query performance; proprietary ROLAPData and aggregates stored in relational database Worst query performance; leverage existing infrastructure HOLAPHybrid approach; detail data stored in relational database, aggregates stored in multidimensional database Best processing performance; query performance better than ROLAP, not as good as MOLAP 8

9. Microsoft BI Platform 9 SQL MDX, DMX RDL

10. Microsoft BI Platform: Development Interfaces  SQL Server Database Engine  Interface: SSMS  SQL Server Integration Services  Interface: SSDT  SQL Server Analysis Services  Interface: SSDT & Excel  SQL Server Reporting Services  Interface: SSDT  MS Office Excel Data Mining Add-In 10

11. Preparing the Data Warehouse for Analysis  Build a multidimensional cube  Refine  Create KPIs…  Design reports  Design mining models  Deploy cube, reports, models to the server 11

12. Building an Analysis Services Cube 12  Create: o Data Source o Data Source View o Cube  Deploy/Process Cube  Use the Cube  OLAP Analysis o Reports o Data Mining Models

13. Internet Example http://www.accelebrate.com/sql_training/ssas_ 2008_tutorial.htm 13