2. Agile data warehouse design
Dao Vo
Confidential

3. Overview of data warehousing
agenda
Overview of data warehousing
Designing and implementing a data warehouse
Waterfall BI/WH development
Agile BI/WH development framework
Q&A
Audience?
Confidential

4. Overview of data warehousing
What is a data warehouse?
Confidential

5. OVERVIEW OF DATA WAREHOUSING
The business problem
What is a data warehouse?
BI/WH Architectures
Confidential

6. Module 1: Introduction to Data Warehousing
Course 10777A
Module 1: Introduction to Data Warehousing
The Business Problem
Key business data is distributed across multiple systems
Emphasize that the drivers for a data warehousing solution are typically business-related rather than technical.
Ask students about the various applications and data stores in their organizations. How easy or difficult is it for students to collate data from them to create an overall, business-wide view of key measures that drive strategic business decision making?

7. Module 1: Introduction to Data Warehousing
Course 10777A
Module 1: Introduction to Data Warehousing
The Business Problem
Finding the information required for business decision making is time-consuming and error-prone
Emphasize that the drivers for a data warehousing solution are typically business-related rather than technical.
Ask students about the various applications and data stores in their organizations. How easy or difficult is it for students to collate data from them to create an overall, business-wide view of key measures that drive strategic business decision making?

8. Module 1: Introduction to Data Warehousing
Course 10777A
Module 1: Introduction to Data Warehousing
The Business Problem
Fundamental business questions are hard to answer
Emphasize that the drivers for a data warehousing solution are typically business-related rather than technical.
Ask students about the various applications and data stores in their organizations. How easy or difficult is it for students to collate data from them to create an overall, business-wide view of key measures that drive strategic business decision making?

9. What Is a Data Warehouse?
Course 10777A
Module 1: Introduction to Data Warehousing
What Is a Data Warehouse?
Explain that this course uses a fairly generic definition for a data warehouse. There are many very specific definitions in use throughout the data warehousing industry, and students may be aware of some of the more common schools of thought with regard to database design, including those of Bill Inmon and Ralph Kimball. This course does not advocate one approach over another, although the lab solutions and data warehouse schema design that are discussed in this course are more in line with a Kimball-based approach than any other.

10. What Is a Data Warehouse?
Course 10777A
Module 1: Introduction to Data Warehousing
What Is a Data Warehouse?
A centralized store of business data for reporting and analysis
Typically, a data warehouse:
Contains large volumes of historical data
Is optimized for querying data (as opposed to inserting or updating)
Is incrementally loaded with new business data at regular intervals
Provides the basis for enterprise business intelligence solutions
Explain that this course uses a fairly generic definition for a data warehouse. There are many very specific definitions in use throughout the data warehousing industry, and students may be aware of some of the more common schools of thought with regard to database design, including those of Bill Inmon and Ralph Kimball. This course does not advocate one approach over another, although the lab solutions and data warehouse schema design that are discussed in this course are more in line with a Kimball-based approach than any other.

11. Designing and implementing a data warehouse
How to design a data warehouse and BI solution?
Confidential

12. DESIGN AND IMPLEMENT WH
Introduction to Dimensional Modeling
Star Schemas
Considerations for Dimension Tables
Considerations for Fact Tables
Snowflake Schemas
Confidential

13. Warehouse Modeling
Confidential

14. Introduction to Dimensional Modeling
Module 3: Designing and Implementing a Data Warehouse
Course 10777A
Introduction to Dimensional Modeling
Business questions focus on measures that are aggregated by business dimensions
Measures are facts about the business
Dimensions are ways in which the measures can be aggregated
Time
Product Line
At this stage, the focus is on identifying the measures and dimensions, not on defining specific fact and dimension tables.
Point out that the measures and dimensions by which the tables will be aggregated are generally identified through discussions with the stakeholders who will use the data warehouse.
Quantity
Revenue
Cost
Profit
Region
Customer
Sales
person
Product

15. Module 3: Designing and Implementing a Data Warehouse
Course 10777A
Module 3: Designing and Implementing a Data Warehouse
Star Schemas
DimSalesPerson
SalesPersonKey
SalesPersonName
StoreName
StoreCity
StoreRegion
Group related dimensions into dimension tables
Group related measures into fact tables
Relate fact tables to dimension tables by using foreign keys
Encourage students to suggest other examples of business events that include measures, and the dimensions by which they may be aggregated.
FactOrders
CustomerKey
SalesPersonKey
ProductKey
ShippingAgentKey
TimeKey
OrderNo
LineItemNo
Quantity
Revenue
Cost
Profit
DimCustomer
CustomerKey
CustomerName
City
Region
DimDate
DateKey
Year
Quarter
Month
Day
DimProduct
ProductKey
ProductName
ProductLine
SupplierName
DimShippingAgent
ShippingAgentKey
ShippingAgentName

16. Module 3: Designing and Implementing a Data Warehouse
Course 10777A
Module 3: Designing and Implementing a Data Warehouse
Snowflake Schemas
DimSalesPerson
SalesPersonKey
SalesPersonName
StoreKey
DimStore
StoreKey
StoreName
GeographyKey
DimDate
DateKey
Year
Quarter
Month
Day
FactOrders
CustomerKey
SalesPersonKey
ProductKey
ShippingAgentKey
TimeKey
OrderNo
LineItemNo
Quantity
Revenue
Cost
Profit
Alert students to the danger that is inherent in refactoring a star schema to a snowflake schema. Most database professionals are experienced in designing OLTP databases, and can have a tendency toward normalization. When you design a data warehouse, you need to keep this tendency in check and ensure that you only create normalized snowflake dimensions when they can be justified by one of the considerations described in this topic.
DimGeography
GeographyKey
City
Region
DimCustomer
CustomerKey
CustomerName
GeographyKey
DimShippingAgent
ShippingAgentKey
ShippingAgentName
DimProductLine
ProductLineKey
ProductLineName
DimProduct
ProductKey
ProductName
ProductLineKey
SupplierKey
DimSupplier
SupplierKey
SupplierName

17. Warehouse Modeling
Confidential

18. Waterfall BI/WH development
Traditional SDLC to develop a BI/WH product
Confidential

19. Waterfall BI/WH development
SDLC Overview
Confidential

20. Waterfall BI/WH development
Confidential

21. SDLC OVERVIEW
Confidential

22. Agile BI/WH development framework
Incremental development framework for BI/WH product
Confidential

23. Agile BI/WH development framework
Agile BI/WH life cycle
Agile DW design overview
Agile ETL Solution
Confidential

24. Agile BI/WH Life Cycle
Confidential

25. Agile BI/WH Life Cycle
Confidential

26. Agile DW design overview
How to design to answer business question?
Confidential

27. Agile DW design overview
How do we ask question?
The 7Ws framework
Design using natural language
Straightforward methodology
Model storming
BEAM methodology
Confidential

28. How do we ask question? Events/Transactions Interrogatives:
A immutable "fact" that occurs in a time and place
Interrogatives:
Who, What, When, Where, Why
Descriptive context that fully describes the event
A set of “dimensions" that describe events
Immutable: Do not change
Confidential

29. The 7Ws framework
Why
Where
How
Who
When
What
How
Many
Confidential

30. The 7Ws framework HOW – FACTs Much Many Often £ $ € Who Customer
Employee
Seller
Organization
What
Product
Service
Transactions
Booking
Event
Why
Causal
Promotion
Reason
Weather
Competition
Where
Location
Geographic
Store
Ship to
Hospital
When
Time
Day
Month
Year
The 7Ws framework

31. Design using natural language
Verbs – Events – Relationships – Fact Tables
Nouns – Details – Entities – Dimensions
Main Clause – Subject-Verb-Object
Prepositions – connect additional details to the main clause
Interrogatives – The 7Ws – Dimension Types
Confidential

32. Straightforward methodology
11111 4
Who 1 5
What
Subject-Verb-Object 2 3
When
Declare Event Type 6
Initial Data Examples
Where 7
How (many)
Quantities - Facts 8
Why
Sufficient Detail Fact Granularity
How 9
Confidential

33. Design using natural language
Verbs – Events – Relationships – Fact Tables
Nouns – Details – Entities – Dimensions
Main Clause – Subject-Verb-Object
Prepositions – connect additional details to the main clause
Interrogatives – The 7Ws – Dimension Types
Confidential

34. Business Event Analysis and Modeling (BEAM✲)
An agile approach to dimensional modeling
Confidential

35. Quick Inclusive Interactive Fun Model Storming Data Modeler
Inclusive: Tinh toan ven
Interactive
Fun
Confidential
Data Modeler
BI Stakeholders

36. BEAM✲ BEAM ✲ Methodology
Structured, non-technical, collaborative working conversation directly with BI Users
BEAM✲
Logical and Physical Dimensional Data Models
Example data
Detailed and Testable ETL Specification
DW Prototype
BI User’s Business Process, Organizational, Hierarchical, and Data Knowledge
Focused Data Profiling
Data Modeler
BI Stakeholders
Confidential

37. Q&A

38. Thank you.