1. Implementing a Data Warehouse with SQL Server Jump Start
Richard Currey | Senior Technical Trainer–New Horizons United
George Squillace | Senior Technical Trainer–New Horizons Great Lakes

2. Meet Richard Currey Senior Technical Trainer – New Horizons United
Focused on database and development technologies
MCDBA, MCITP Dev / Admin / BI, MCSE Data Platform, BI
MCSD, MCPD Web Developer, ASP .NET Developer, Windows Developer
28 Years Industry Experience
Designed, developed and managed BI-centric projects at several Fortune 500 organizations
Extensive consulting and project management background

3. Meet George SQUILLACE 28 Years Industry Experience
Senior Technical Trainer – New Horizons Great Lakes (20 Years)
“SQL” is in his name!
Focused on database technologies
MCT Since 1997
28 Years Industry Experience
SQL Server 2012: certified as MCSA, MCSE: Data Platform, & MSCE: Business Intelligence
Certified in every version of SQL Server since SQL 2000
Certified in every version of Windows Server from NT through Windows Server 2008
Certified in Exchange Server 5.5 through Exchange Server

4. Course Modules Implementing a Data Warehouse with SQL Server
01 | Design and Implement Dimensions and Fact Tables
04 | Control Flow
02 | Data Flow - Extract Data
05 | Configure and Deploy SSIS
03 | Data Flow - Transform Data
06 | Manage Enterprise Data

5. Setting Expectations Target Audience
Data warehousing specialists who want to expand their knowledge of SQL Server Integration Services (SSIS)
Database professionals who want to take exam and get certified in data warehouse implementations
Suggested Prerequisites/Supporting Material
SQL Server development experience and exposure to extract, transform, and load (ETL) processes
Course 10777, Implementing a Data Warehouse with Microsoft SQL Server 2012
MS Press Book: Training Kit (Exam ): Implementing a Data Warehouse with Microsoft SQL Server 2012

6. 01 | Design and Implement Dimensions and Fact Tables
Richard Currey | Senior Technical Trainer–New Horizons United
George Squillace | Senior Technical Trainer–New Horizons Great Lakes

7. Module 1 Overview Schema Design: Star vs. Snowflake
Facts and Fact Tables
Fact and Dimension Granularity
Conformed and Non-Conformed Dimensions
Time Dimensions

8. Topic: Schema Design: Star vs. Snowflake

9. Topic: Schema Design: Star versus Snowflake
Star Schema
Snowflake Schema
Processing and Performance Considerations

10. Star Schema A star schema has a single table for each dimension
DimSalesPerson
SalesPersonKey
SalesPersonName
StoreName
StoreCity
StoreRegion
DimProduct
ProductKey
ProductName
ProductLine
SupplierName
DimCustomer
CustomerKey
CustomerName
City
Region
FactOrders
ShippingAgentKey
TimeKey
OrderNo
LineItemNo
Quantity
Revenue
Cost
Profit
DimDate
DateKey
Year
Quarter
Month
Day
DimShippingAgent
ShippingAgentName
A star schema has a single table for each dimension
Each table supports all attributes for that dimension
Typically a de-normalized solution

11. Implementing a Star Schema

12. Snowflake Schema More normalized solution
DimSalesPerson
SalesPersonKey
SalesPersonName
StoreKey
DimProduct
ProductKey
ProductName
ProductLineKey
SupplierKey
DimCustomer
CustomerKey
CustomerName
GeographyKey
FactOrders
ShippingAgentKey
TimeKey
OrderNo
LineItemNo
Quantity
Revenue
Cost
Profit
DimDate
DateKey
Year
Quarter
Month
Day
DimShippingAgent
ShippingAgentName
DimProductLine
ProductLineName
DimGeography
City
Region
DimSupplier
SupplierName
DimStore
StoreName
More normalized solution
Typically contains multiple tables per dimension
Each table contains dimension key, value, and the foreign key value for the parent

13. Implementing a Snowflake Schema

14. Processing and Performance Considerations
Star schema requires de-normalization during the load process
Can impact the ETL times
Snowflake schema can increase dimension complexity
Can impact Analysis Services solutions, negatively affecting cube performance

15. Topic: Facts and Fact Tables

16. Topic: Facts and Fact Tables
What Is a Fact?
Grouping Facts
What Is Granularity?
Design Considerations

17. What Is a Fact?
Facts are the key metrics used to measure business results:
Sales
Production
Inventory
Can be additive, semi-additive, or non-additive

18. Grouping Facts Facts are grouped into fact tables
Related facts should be in the same fact table
Facts with different granularity should be in different tables

19. What Is Granularity?
Granularity refers to the level of detail in which facts are recorded
Facts can be at different levels of granularity

20. Design Considerations
Fact tables should have all keys relating to dimensions
Primary key should be composite of all dimension keys
Separate additive, semi-additive, and non-additive facts

21. Topic: Fact and Dimension Granularity

22. Topic: Fact and Dimension Granularity
How to Determine Fact Granularity
Dimension Granularity

23. How to Determine Fact Granularity
Granularity is determined based on business needs
Should be the lowest level of detail that needs to be examined
If data from transactional systems has more detail than needed for analysis, ETL should aggregate the details

24. Dimension Granularity
Dimension granularity needs to be matched with fact granularity
Each dimension has its own granularity
Fact tables are keyed to the granularity of the dimensions

25. Topic: Conformed and
Non-Conformed Dimensions

26. Topic: Conformed and Non-Conformed Dimensions
What Are Conformed and Non-Conformed Dimensions?
Shared and Degenerate Dimensions
What Is a Slowly Changing Dimension?

27. What Are Conformed and Non-Conformed Dimensions?
Shared by multiple fact tables
Used when all business users have the same definitions for the dimension
Non-conformed dimension
Dimension table targeted to a single fact table
Used when dimensions have different definitions for different business units

28. Creating Conformed and Non-Conformed Dimensions

29. Shared and Degenerate Dimensions
Shared dimension
Used by multiple facts
Dimension key is stored in the fact table
Dimension value is stored in the dimension table with other attributes of that dimension
Degenerate dimension
Used by a single fact table
Dimension value is stored directly in the fact table
No corresponding dimension table

30. What Is a Slowly Changing Dimension?
When the historical attribute values are retained if the attributes are updated
Used when the organization does not want to lose track of what actually happened
Example: customer moves from Connecticut to Seattle
Slowly changing dimension types:
Type 1: Attribute history is not retained
Type 2: Attribute change creates a new record
Type 3: Original attribute value recorded and latest value recorded with an effective date

31. Implementing a Slowly Changing Dimension

32. Topic: Time Dimensions

33. Topic: Time Dimensions
Types of Time Dimensions
Time Dimensions and Hierarchies

34. Types of Time Dimensions
Based on standard calendar breakdowns
Year => Month => Day
Year => Quarter => Week => Day
Based on fiscal calendar
Year => Fiscal Quarter => Fiscal Month => Fiscal Week => Day
Time dimension needs to contain all hierarchy elements to the lowest granularity for the fact tables

35. Time Dimensions and Hierarchies
Establishes the “buckets” that the business uses
Typically there are multiple hierarchies in the dimension
Calendar
Business
Created using Microsoft Excel, scripts, or are auto-generated

36. Creating a Time Dimension