Synopsis: [Video link: http://www.youtube.com/watch?v=ZNrTxSU5IQ0 ] Jim Stagnitto and John DiPietro of consulting firm a2c) will discuss Agile Data Warehouse Design - a step-by-step method for data warehousing / business intelligence (DW/BI) professionals to better collect and translate business intelligence requirements into successful dimensional data warehouse designs. The method utilizes BEAM✲ (Business Event Analysis and Modeling) - an agile approach to dimensional data modeling that can be used throughout analysis and design to improve productivity and communication between DW designers and BI stakeholders. BEAM✲ builds upon the body of mature "best practice" dimensional DW design techniques, and collects "just enough" non-technical business process information from BI stakeholders to allow the modeler to slot their business needs directly and simply into proven DW design patterns. BEAM✲ encourages DW/BI designers to move away from the keyboard and their entity relationship modeling tools and begin "white board" modeling interactively with BI stakeholders. With the right guidance, BI stakeholders can and should model their own BI data requirements, so that they can fully understand and govern what they will be able to report on and analyze. The BEAM✲ method is fully described in Agile Data Warehouse Design - a text co-written by Lawrence Corr and Jim Stagnitto. About the speaker: Jim Stagnitto Director of a2c Data Services Practice Data Warehouse Architect: specializing in powerful designs that extract the maximum business benefit from Intelligence and Insight investments. Master Data Management (MDM) and Customer Data Integration (CDI) strategist and architect. Data Warehousing, Data Quality, and Data Integration thought-leader: co-author with Lawrence Corr of "Agile Data Warehouse Design", guest author of Ralph Kimball’s “Data Warehouse Designer” column, and contributing author to Ralph and Joe Caserta's latest book: “The DW ETL Toolkit”. John DiPietro Chief Technology Officer at A2C IT Consulting John DiPietro is the Chief Technology Officer for a2c. Mr. DiPietro is responsible for setting the vision, strategy, delivery, and methodologies for a2c’s Solution Practice Offerings for all national accounts. The a2c CTO brings with him an expansive depth and breadth of specialized skills in his field. Sponsor Note: Thanks to: Microsoft NERD for providing awesome venue for the event. http://A2C.com IT Consulting for providing the food/drinks. http://Cognizeus.com for providing book to give away as raffle.

1. Agile Data Warehouse Design with Big Data
John DiPietro & Jim Stagnitto!1

2. Agenda
• Introduction / a2c Overview
• Modeling for End Users
• Role of Dimensional Models in Big Data
• Example: eCommerce
• Structured Data: Sales
• Semi-structured Data: Clickstream
• Agile Dimensional Modeling Overview
• Case Study Review
• Q&A
!2

3. Introduction
• a2c
• Boutique EDM (Enterprise Data Management)
consultancy firm:
• Data Warehousing
• Master Data Management
• Closed Look Analytics and Visualization
• Data & Application Architecture
• John DiPietro
• Principal, Chief Technology Officer
• Jim Stagnitto
• Data Warehouse & MDM Architect
!3

4. a2c Corporate Overview
& Industry Experience
!4

5. Company Overview
• Technology Solution Consultancy headquartered in Philadelphia with
regional offices in New York and Boston
• Servicing Healthcare, Life Science, Tel-Com and Financial Services
industries with recent obtainment of our GSA schedule to pursue Federal
Government opportunities
• Consultant base of over 2500 proven IT professionals throughout the North
East Region with a recruiting network which provides national coverage
• Flexible approach to helping our clients with their initiatives
• Project-based Solutions
• Staff Augmentation
• Managed Service Offerings – “On-Shore QA , Development & Application Support”
• Executive & Professional Search
!5

6. Competitive Advantage
• Founders of a2c were part of the fastest growing privately held IT consulting and staff
augmentation firm in the US from 1994-2002. Our Executive Management Team has over a
100 years collective experience and been responsible for delivering over a half-billion dollars
of IT Consulting and staff augmentation revenue from 1994 through to the present day.
• a2c’s Recruiting Engine and Methodology is one of the best in the industry, capable of
producing quality results, on-demand for our clients
• Resource Managers continually “Silo” disciplines with available candidates whom have
proven their abilities with us over the last 10 years
• Our solutions organization is instrumentally involved during the screening and selection
process to ensure that candidates submitted to our clients are an ideal match
• a2c’s Culture provides an ability to attract and retain the best talent in the industry and fosters
creativity, integrity, growth and teamwork
• a2c provides our clients with an alternative solution to a “Big 4” consultancy at substantial
savings for projects that are between $500K and $5M due to our flexibility, agility and focus
!6

7. Representative Clients
03/19/12
!7

8. a2c Solution Engagement Structures
• Technology Strategy & Roadmap Formulation
• Needs & Readiness Assessment
• Package & Platform Selections
• Proof of Concept Implementation
• Requirements Discovery & Specifications
• Program/Project Management
• Full Life Cycle & Application Development
• Infrastructure & Facilities Initiatives
• Managed Services & Maintenance Support
!8

9. a2c Solutions Capabilities
• Enterprise Data Management Practice helps clients manage their complete Information
Lifecycle from their On-line Transactional systems to their Data Warehousing, Enterprise
Reporting, Data Migration, Back-Up and Recovery Strategies (See Slide 7)
• Business Architecture & Optimization Practice utilizes “Six Sigma Lean” methodologies to
analyze, re-engineer and automate our client’s business processes to leverage human
workflow and business rules engine technologies to create efficiencies and provide
business unit owners with the necessary metrics to continually improve performance
• Program Management Office oversees all aspects of solutions planning and delivery
across client engagement teams and provides the methodology and frameworks which
are based on PMI® industry standards
• Application Development & Managed Services Practice helps clients architect, implement
and deploy the latest Microsoft and Enterprise Java based applications which are built on
proven frameworks and architectures for the enterprise
• a2c's SDLC Delivery Model is comprised of over 20 years collective best practices and
industry proven methodologies that allow our delivery teams to rapidly design, develop
and implement solutions. Our SDLC model has been designed to complement our project
management methodology, utilizing iterative development cycles that enable project
teams to provide consistently high quality, on-time deliverables, regardless of technology
platform
!9

10. Agile DW Design
Overview
!10

11. Modeling for End Users
• How to Design to Answer
Business Questions?
• Think about how questions are articulated
• And how the answers should be
deliveredIdentify a common question
framework
• Design an architecture that
embraces and leverages this
common question framework
• Utilize the best designs and
technologies to:
• (a) derive the answers
• (b) present them in compelling ways that
lead to the next interesting question!
!11

12. What
How Do We Ask Questions?
“How do this quarter’s sales by sales rep of
electronic products that we promoted to retail
customers in the east compare with last year’s?
What
Who
Who
When
WhenWhere Why
!12

13. How Do We Ask Questions?
• Events / Transactions
• e.g. Sale
• a immutable "fact" that occurs in a time and (typically a)
place
• Interrogatives:
• Who, What, When, Where, Why
• Descriptive context that fully describes the event
• a set of “dimensions" that describe events
!13

14. Dimensional Value Proposition
• It makes sense to present answers to people using the same
taxonomy of events and interrogatives (aka: facts and dimensions
- dimensional structure) that they use when forming questions
• Events are instances of processes :
• It’s best to present information to people who will ask the system
questions in dimensional form
• This is true regardless of the type of information being
interrogated, it’s source, or IT stuff (like database technologies
utilized)
• It’s best to model this presentation layer based on the events (aka:
business processes) that underlie the questions
!14

15. How
Many
Why
Where How
WhoWhen
What
!15

16. Scenarios
• A brief discussion of how and where
dimensional modeling and/or
databases fit within common and
emerging “big data” data
warehousing architectures
!16

17. Kimball Dimensional DW
Dimensional BI Semantic Layer
Dimensional Data Warehouse
Data Movement / Integration
Source Data
(Structured)
!17

18. Kimball with Big Data
Dimensional BI Semantic Layer
Dimensional Data Warehouse
Data Movement / Integration Tier
Source Data Tier
(Un/Semi-Structured)
Big Data
Capture
(e.g. HDFS)
Big Data
Discovery
(e.g. MR)
Data Movement / Integration Tier
Source Data Tier
(Structured)
!18

19. Corporate Information Factory (CIF)
Dimensional BI Semantic Layer
Dimensional Tier
(Virtual or Physical)
Data Movement / Integration
Source Data
(Structured)
Corporate Information Factory 3NF DW
!19

20. CIF with Big Data
Dimensional BI Semantic Layer
Dimensional Tier
(Virtual or Physical)
Data Movement / Integration Tier
Source Data Tier
(Un/Semi-Structured)
Big Data
Capture
(e.g. HDFS)
Big Data
Discovery
(e.g. MR)
Data Movement / Integration Tier
Source Data Tier
(Structured)
Corporate Information
Factory 3NF DW
!20

21. Data Vault
Dimensional BI Semantic Layer
Dimensional Tier
(Virtual or Physical)
Data Movement / Integration
Source Data
(Structured)
Data Vault
!21

22. Data Vault with Big Data
Dimensional BI Semantic Layer
Dimensional Tier
(Virtual or Physical)
Data Movement / Integration Tier
Source Data Tier
(Un/Semi-Structured)
Big Data
Capture
(e.g. HDFS)
Big Data
Discovery
(e.g. MR)
Data Movement / Integration Tier
Source Data Tier
(Structured)
Data Vault
!22

23. Etc.
!23

24. Common Framework
Dimensional BI Semantic Layer
Dimensional Tier
[Physical (Kimball) or Virtual (CIF or Data Vault)
Un/Semi-Structured Data
Movement
Un/Semi-Structured Source Data
Persistant Un/
Semi-Structured
Staging Area
Unstructured ->
Structured
Data Discovery
Processing
Structured Data Movement
Structured Source Data
(Structured)
Persistent Structured Data
Repository
(not needed for Kimball)
!24
Insight
Generation /
Data Mining

25. Kitchen
Off Limits to End Users
Data Professionals Only Please
Dangerous / Inhospitable Environment
Data Assets “Not Ready for Primetime”
Structured Variably For Data Processing
Dining Room
Readily Accessible to End Users
(and BI Developers)
Safe, Hospital Environment
Data Assets “Ready for Primetime”
Dimensionally Structured
Common Framework
Dimensional BI Semantic Layer
Dimensional Tier
[Physical (Kimball) or Virtual (CIF or Data Vault)
Un/Semi-Structured Data Movement
Un/Semi-Structured Source Data
Persistant Un/
Semi-Structured
Staging Area
Unstructured ->
Structured Data
Discovery
Processing
Structured Data Movement
Structured Source Data
(Structured)
Persistent Structured Data
Repository
(not needed for Kimball)
eCommerce ExampleClickstream Data eCommerce Sale
!25

26. eCommerce Example: Clickstream
Raw Clickstream Data!
25 52 164 240 274 328 368 448 538 561 630 687 730 775 825
834
39 120 124 205 401 581 704 814 825 834
35 249 674 712 733 759 854 950
39 422 449 704 825 857 895 937 954 964
15 229 262 283 294 352 381 708 738 766 853 883 966 978
26 104 143 320 569 620 798
7 185 214 350 529 658 682 782 809 849 883 947 970 979
227 390
71 192 208 272 279 280 300 333 496 529 530 597 618 674 675
720 855 914 932
183 193 217 256 276 277 374 474 483 496 512 529 626 653 706
878 939
161 175 177 424 490 571 597 623 766 795 853 910 960
125 130 327 698 699 839
392 461 569 801 862
27 78 104 177 733 775 781 845 900 921 938
101 147 229 350 411 461 572 579 657 675 778 803 842 903
71 208 217 266 279 290 458 478 523 614 766 853 888 944 969
43 70 176 204 227 334 369 480 513 703 708 835 874 895
25 52 278 730
151 432 504 830 890
71 73 118 274 310 327 388 419 449 469 484 706 722 795 810
844 846 918
130 274 432 528 967
188 307 326 381 403 523 526 722 774 788 789 834 950 975
89 116 198 201 333 395 653 720 846
70 171 227 289 462 538 541 623 674 701 805 946 964
143 192 317 471 487 631 638 640 678 735 780 865 888 935
17 242 471 758 763 837 956
52 145 161 283 375 385 676 721 731 790 792 885
182 229 276 529
43 522 565 617 859
Semi-Structured
Recording of every page request
made by a user
Includes some structural elements –
such as when the request was
made and who the user is
Requires significant prep work in
order to fit into a traditional row-
based relational database
Apples and Oranges: Pre-
Sessionized Page Visits, Detailed
Product Views, Catalogue
Requests, Shopping Cart Adds /
Deletes / Abandons, etc.
Needs to be converted into
seperate-but-relatable dimensional
facts - with many shared
(conformed) dimensions
!26

27. Typical Clickstream “Page View” Dimensional
Model
What
Why Who
When
What
!27

28. eCommerce Example: Web Sales
• Fully Structured
• The Sale Transaction
typically carries all
fundamental dimensions:
• Time
• Customer
• Referring URL / Search
Phrase
• Product
• Purchase and/or Shipment
(Geo or URL) Locations
• Promotion / Campaign
• Etc.
• And “How Many”
Measures
• Unit and Price Quantities /
Amounts
• Discount Amounts
• Etc
!28

29. eCommerce Dimensionality
Facts (below) &
Dimensions (right)
Time!
(When)
Customer!
(Who)
Web Page!
(Where)
Product!
(What)
Referring
URL!
(Where)
Promotion
/
Campaign
(Why)
Activity
Type
(How)
Page Visit
View Start
View End
Session
Start
Session End
Visitor
Current
Previous
Next
✔
Detailed Product
View
View Start
View End
Session
Start
Session End
Prospect
Current
Previous
Next
✔ ✔
Shopping Cart
Activity
Activity Start
Activity End
Prospect ✔ ✔ ✔ ✔
Sale (Checkout)
Sale Start
Sale End
Customer ✔ ✔ ✔ ✔
Shipment / Delivery
Shipment
Delivery
Customer
Delivery
Recipient
✔
!29

30. Agile DW Design
Overview
!30

31. The first dimensional modeler:
R.K.Ralph Kimball?Rudyard Kipling
!31

32. –Rudyard Kipling
I keep six honest serving-men
(They taught me all I knew);
Their names are What and Why and When
And How and Where and Who…
!32
!32

33. Who
!33

34. What
!34

35. When
!35

36. Where
!36

37. Why
!37

38. How
!38

39. How Many
!39

40. The 7WsFramework

41. How
Many
Why
Where How
WhoWhen
What

42. How did we get here?

43. Corporate Information
Factory
!
Data-Driven Analysis
Undisciplined Dimensional
!
Report-Driven Analysis
Dimensional Bus
Architecture
!
Process-Driven Analysis
DW Architectures: A Brief History

44. 7Ws Dimensional Model
How – Facts:
Much
Many
Often
£ $ €
Where
Location
Geographic
Store
Ship To
Hospital
Who
Customer
Employee
Third Party
Organization
What
Product
Service
Transactions
When
Time
Day
Month
Fiscal Period
Why
Causal
Promotion
Reason
Weather
Competition
??

45. Where
WhoWhen
What
HowWhy
How
Many
BEAMBusiness Event Analysis & Modeling

46. How
do you design a data warehouse?

47. Tech Design Artifacts?
CALENDAR
Date Key
Date
Day
Day in Week
Day in Month
Day in Qtr
Day in Year
Month
Qtr
Year
Weekday Flag
Holiday Flag
PRODUCT
Product Key
Product Code
Product Description
Product Type
Brand
Subcategory
Category
PROMOTION
Promotion Key
Promotion Code
Promotion Name
Promotion Type
Discount Type
Ad Type
SALES FACT
Quantity Sold
Revenue
Cost
Basket Count
Date Key
Product Key
Store Key
Promotion Key
STORE
Store Key
Store Code
Store Name
URL
Store Manager
Region
Country

48. OK, NowValidate with

49. Why
Agile Data Warehousing?

50. Waterfall BI/DW
Analysis
Design
Development
Test
Release
Limited Stakeholder interaction
DATA
VALUE?Data
Model
Stakeholder
Input
ETL BIRequirements
BDUF
Next YearThis Year

51. Agile DW/BI Development
Iteration nIteration …Iteration 3Iteration 1
VALUE!VALUE? VALUE VALUE! VALUE!
Iteration 2
Stakeholder interaction
Next YearThis Year
Review
Release
BI
Prototyping
ETL
?
RevBIETLADM
JEDUF
DATA

52. State of The
DW Field
Solid:
Dimensional Data Warehouse Design is Mature
Proven Design Patterns Exist for Common
Requirements
Hit or Miss:
Collecting Unambiguous and Thorough
Requirements
Slotting Requirements into Proven Design
Patterns
End-User Ownership and Validation
Too Often: Snatching Defeat from the Jaws of
Victory
!52

53. Quick
Modelstorming
Data
Modeler BI Stakeholders
Inclusive
Interactive Fun

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

55. Requirements =
Design
55

56. Collaboration at Every
Step

57. Agile Data Modeling Requirements
• Techniques for encouraging interaction
• Must use simple, inclusive notation and tools
• Must be quick: hours rather than days – modelstorming
• Balance ‘just in time’ (JIT) and ‘just enough design up
front’ (JEDUF) to reduce design rework
• DW designers must embrace data model change, allow models
to evolve, avoid generic data models; need design patterns they
can trust to represent tomorrow’s BI requirements tomorrow
• ETL and BI developers must embrace database change; need
tool support
!57

58. Whatkind of model?

60. CALENDAR
Date Key
Date
Day
Day in Week
Day in Month
Day in Qtr
Day in Year
Month
Qtr
Year
Weekday Flag
Holiday Flag
PRODUCT
Product Key
Product Code
Product Description
Product Type
Brand
Subcategory
Category
PROMOTION
Promotion Key
Promotion Code
Promotion Name
Promotion Type
Discount Type
Ad Type
SALES FACT
Quantity Sold
Revenue
Cost
Basket Count
Date Key
Product Key
Store Key
Promotion Key
STORE
Store Key
Store Code
Store Name
URL
Store Manager
Region
Country

61. Customer
Country
Customer Type
Product Type
Category
Product
Month
Calendar
Holiday Type
Store Type
Store
City
Sales Fact

62. Modeling by Abstraction

63. Modeling by Example

64. Agile DW Design
Process
64

65. Who does what?
SubjectsVerb Objects
“Customers buy products”
BEAM✲
Modeler
BI Users
Collaborative / Conversational Design

66. 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
• Business Vocabulary - no IT-Speak
!66

67. “Spreadsheet”-like Models
Details
Example Data (4-6
rows)
Subject Column Name
Object Column Name
Verb
Interrogative
Event Table Name (filled in later)

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

69. Capture Example Data
Engage business users
Clarify definitions / Conform Dimensions
Illustrate exceptions
Drive out uniqueness
“Show and tell”
verb on/at/every
SUBJECT OBJECT EVENT
DATE
[who] [what] [when] [where] [how many] [why] [how]
Typical Typical/Popular Typical Typical Typical/Average Typical/Normal Typical/Normal
Different Different Different Different Different Different Different
Repeat Repeat Repeat Repeat Repeat Repeat Repeat
Missing Missing Missing Missing Missing Missing Missing
Group Multiple/Bundle Multi-Level Multiple Values
Old, Low Old, Low Value Oldest needed Near Min, Negative, 0
New, High New, High Most Recent, Future Far Max, Precision Exceptional Exceptional

70. Thoughtful Example Data
Detailed ETL
Specification

71. Identify Event Type Early

72. Adjust Conversation Based on Event Type
• Discrete Event - Transaction
• Instantaneous/short duration, irregularly occurring events or
transactions
• Recurring Event - Periodic Snapshot – measurement
• Regularly occurring events, ongoing processes, typically use to
measure cumulative of discrete events
• Evolving Event - Accumulating Snapshot – timeline
• Non-instantaneous/longer duration, irregularly occurring events or
transactions
• Represents current status - reflects adjustments
!72

73. Capture When Details
When do Customers order Products?
BEAM✲
Modeler
BI Users
“On the Order Date”

74. Any other Whens?

75. Any other Whos?

76. And so on...

77. Model How Many Measures
• Additive – can be summed up over any combination
of dimensions. No special rules
• Non-additive – can not be summed over any
dimension e.g. unit price or temperature
• Must be aggregated in other ways e.g. average, min, max
• Degenerate Dimensions – transaction #, timestamps, flags
• Semi-additive – can not be summed across at least
one dimension e.g. balances can not be summed
over time
!77

78. Modeling Dimensions

79. Annotate w Targeted Data Profiling

80. Proceed Through the Business ProcessValue Chain

81. Collaborative Dimension Conformance
Dimensions
TimeShipperCustomerPlant Response Product Promotion
Sales
Campaigns

82. Identify Hierarchy Types
Balanced
Complex
Simple
Ragged Variable
Depth

83. Graphically Depict Hierarchies

84. Visualize The Hierarchies

85. Paint The Organization

86. Prototype! Not “Data Model Review”

87. Recap
• Collaborative and Agile
• Data Modeling
• Data Sourcing
• Data Conformance
• Requirements = Design
• Slots directly into proven and mature dimensional data warehousing
design patterns
• Validation through Prototyping
• Semi-automated build of dimensional data warehouse
• Perfect compliment to Agile BI Tools and Methods (e.g. Pentaho)
!87

88. If you have been affected by
any of the issues raised
in this presentation

89. !
Agile Data Warehouse Design
Lawrence Corr, Jim Stagnitto, Decision Press, November 2011
!

90. Questions / Comments