1. Designing Business Intelligence Solutions with Microsoft SQL Server
Chris Testa-O’Neill | Principal Consultant | Claribi
Charley Hanania | Principal Consultant | QS2 AG – Quality Software Solutions

2. Meet Chris Testa-O’Neill | ‏@ctesta_oneill

3. Meet Charley Hanania | ‏@charleyhanania
MCT Regional Lead - Switzerland
Regional Mentor - Western Europe
Chapter Leader - Switzerland
Joint Country Lead - Switzerland

4. Course Topics Implementing Data Models
and Reports with Microsoft SQL Server
01 | Planning a SQL Server BI Solution
04 | Design an ETL Solution
02 | Designing a BI Infrastructure
05 | Design BI Data Models
03 | Design a Data Warehouse
06 | Designing Reporting Services Solutions

5. Setting Expectations Target Audience
Business Intelligence Architects, Developers
Suggested Prerequisites/Supporting Material
A basic understanding of dimensional modeling (star schema) for data warehouses
The ability to create Integration Services packages that include control flows and data flows
The ability to create a basic multidimensional cube with Analysis Services
The ability to create a basic tabular model with PowerPivot and Analysis Services
The ability to create Reporting Services reports with Report Designer

6. 01 | Planning a SQL Server BI Solution
Chris Testa-O’Neill | Principal Consultant | Claribi
Charley Hanania | Principal Consultant | QS2 AG – Quality Software Solutions

7. Module Overview Gathering Requirements.
Components of a BI Infrastructure
Plan a Data Warehouse.
Plan ETL Infrastructure.
Plan Data Models.
Plan Reporting Services Infrastructure.

8. Gathering Requirements

9. Gathering Requirements
Business:
Goals
Objectives
Budgets
Timescales
Operations time
Compliance
Legal
Technical:
Functional
Performance
Availability
Scalability
Disaster Recovery

10. Components of a BI Infrastructure

11. Components of a BI Infrastructure
Data
Warehouse
Master Data Management
Data Cleansing
Data Sources 
ETL
Data Models
Reporting and Analysis

12. Plan for a Data Warehouse

13. Plan for a Data Warehouse
Reporting and Analysis
Kimball Dimensional Data Marts
Inmon Corporate Information Factory
Central Dimensional Data Warehouse
Federated Hub and Spoke
Data Cleansing
Data Models
Data Sources
Data
Warehouse 
ETL
Master Data Management

14. Plan an ETL Infrastructure

15. Plan an ETL Infrastructure
Reporting and Analysis
Enterprise Integration Management
ETL:
Extract from sources
Transform schema & content
Load into destination
Data Cleansing:
Data value validation
Duplicate record matching
Master Data Management:
Business entity integrity
Data Cleansing
Data Models
Data Sources
Data
Warehouse 
ETL
Master Data Management

16. Plan Data Models

17. Plan Data Models  Data Sources Data Warehouse ETL
Reporting and Analysis
Benefits of data models:
Abstract data warehouse tables
Simplify analysis for users
Add business logic
Pre-aggregate measures
Provide a standard interface
Types of models:
Multidimensional
Tabular
Data Cleansing
Data Models
Data Sources
Data
Warehouse 
ETL
Master Data Management

18. Plan Reporting Services Infrastructure

19. Plan Reporting Services infrastructure
IT-provided reports
Self-service reporting
Interactive analysis
Dashboards and scorecards
Data mining
Reporting and Analysis
Data Cleansing
Data Models
Data Sources
Data
Warehouse 
ETL
Master Data Management