Agile Data Warehouse Design for Big Data Presentation
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The document discusses agile data warehouse design with a focus on big data, highlighting its importance in business analytics and modeling for end users. It outlines the practices and methodologies adopted by A2C, a consultancy firm specializing in data management across various industries. Key components include dimensional modeling, the integration of structured and semi-structured data, and the use of effective frameworks for data analysis and visualization.
![Capture Example Data
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
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Different
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Different
Different
Repeat
Repeat
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Repeat
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Multiple/Bundle
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Old, Low Value
Oldest needed
Near
Min, Negative, 0
New, High
New, High
Most Recent, Future
Far
Max, Precision
Multi-Level
Engage business users
Clarify definitions / Conform Dimensions
Illustrate exceptions
Drive out uniqueness
“Show and tell”
Multiple Values
Exceptional
Exceptional](https://image.slidesharecdn.com/a2cbostonbigdatameetup20131114-131116193233-phpapp02/85/Agile-Data-Warehouse-Design-for-Big-Data-Presentation-69-320.jpg)
Agile Data Warehouse Design for Big Data Presentation
- 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 • • Data Warehousing • Master Data Management • Closed Look Analytics and Visualization • • Boutique EDM (Enterprise Data Management) consultancy firm: 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
- 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
- 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. How Do We Ask Questions? Who What When “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 Where Why !12 When
- 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
- 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 Big Data Capture Big Data Discovery (e.g. HDFS) (e.g. MR) Data Movement / Integration Tier Data Movement / Integration Tier Source Data Tier Source Data Tier (Un/Semi-Structured) (Structured) !18
- 19. Corporate Information Factory (CIF) Dimensional BI Semantic Layer Dimensional Tier (Virtual or Physical) Corporate Information Factory 3NF DW Data Movement / Integration Source Data (Structured) !19
- 20. CIF with Big Data Dimensional BI Semantic Layer Dimensional Tier (Virtual or Physical) Big Data Capture Big Data Discovery (e.g. HDFS) (e.g. MR) Corporate Information Factory 3NF DW Data Movement / Integration Tier Data Movement / Integration Tier Source Data Tier Source Data Tier (Un/Semi-Structured) (Structured) !20
- 21. Data Vault Dimensional BI Semantic Layer Dimensional Tier (Virtual or Physical) Data Vault Data Movement / Integration Source Data (Structured) !21
- 22. Data Vault with Big Data Dimensional BI Semantic Layer Dimensional Tier (Virtual or Physical) Big Data Capture Big Data Discovery (e.g. HDFS) (e.g. MR) Data Vault Data Movement / Integration Tier Data Movement / Integration Tier Source Data Tier Source Data Tier (Un/Semi-Structured) (Structured) !22
- 23. Etc. !23
- 24. Common Framework Dimensional BI Semantic Layer Dimensional Tier [Physical (Kimball) or Virtual (CIF or Data Vault) Persistant Un/ Semi-Structured Staging Area Unstructured -> Structured Data Discovery Processing Persistent Structured Data Repository (not needed for Kimball) Un/Semi-Structured Data Movement Structured Data Movement Un/Semi-Structured Source Data Structured Source Data (Structured) !24 Insight Generation / Data Mining
- 25. Common Framework Dining Room Readily Accessible to End Users (and BI Developers) Safe, Hospital Environment Data Assets “Ready for Primetime” Dimensionally Structured Dimensional BI Semantic Layer Dimensional Tier [Physical (Kimball) or Virtual (CIF or Data Vault) Persistant Un/ Semi-Structured Staging Area Unstructured -> Structured Data Discovery Processing Persistent Structured Data Repository Kitchen (not needed for Kimball) Un/Semi-Structured Data Movement Structured Data Movement Un/Semi-Structured Source Data Structured Source Data (Structured) Clickstream Data Off Limits to End Users Data Professionals Only Please Dangerous / Inhospitable Environment Data Assets “Not Ready for Primetime” Structured Variably For Data Processing eCommerce Sale eCommerce Example !25
- 26. eCommerce Example: Clickstream 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 rowbased relational database Apples and Oranges: PreSessionized 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 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
- 27. Typical Clickstream “Page View” Dimensional Model What When What Who Why !27
- 28. eCommerce Example: Web Sales • • Time • Customer • • Referring URL / Search Phrase • Promotion / Campaign • The Sale Transaction typically carries all fundamental dimensions: Purchase and/or Shipment (Geo or URL) Locations • Fully Structured • • Etc. And “How Many” Measures • • !28 Discount Amounts • Product Unit and Price Quantities / Amounts Etc
- 29. eCommerce Dimensionality Facts (below) & Dimensions (right) Page Visit Detailed Product View Shopping Cart Activity Time! (When) View Start View End Session Start Session End View Start View End Session Start Session End Activity Start Activity End Customer! Web Page! (Who) (Where) Visitor Current Previous Next Prospect Current Previous Next Product! (What) Referring URL! (Where) Promotion / Campaign (Why) Activity Type (How) ✔ ✔ ✔ Prospect ✔ ✔ ✔ ✔ ✔ ✔ ✔ Sale (Checkout) Sale Start Sale End Customer ✔ Shipment / Delivery Shipment Delivery Customer Delivery Recipient ✔ !29
- 31. The first dimensional modeler: Rudyard Kipling Ralph Kimball? R.K. !31
- 32. 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… –Rudyard Kipling !32 !32
- 33. Who !33
- 34. What !34
- 35. When !35
- 36. Where !36
- 37. Why !37
- 38. How !38
- 39. How Many !39
- 42. How did we get here?
- 43. DW Architectures: A Brief History Corporate Information Factory ! Data-Driven Analysis Undisciplined Dimensional ! Report-Driven Analysis Dimensional Bus Architecture ! Process-Driven Analysis
- 44. 7Ws Dimensional Model When Who Time Customer Day How – Facts: Employee Month Much Third Party Fiscal Period Many Organization Often £$€ Where What Location Product ?? Why Service Store Causal Transactions Ship To Promotion Hospital Reason Geographic Weather Competition
- 46. How do you design a data warehouse?
- 47. Tech Design Artifacts? CALENDAR PRODUCT Date Key Product Key Date Day Day in Week Day in Month Day in Qtr Day in Year Month Qtr Year Weekday Flag Holiday Flag Product Code Product Description Product Type Brand Subcategory Category SALES FACT Date Key Product Key Store Key Promotion Key Quantity Sold Revenue Cost Basket Count STORE PROMOTION Store Key Promotion Key Store Code Store Name URL Store Manager Region Country Promotion Code Promotion Name Promotion Type Discount Type Ad Type
- 48. OK, Now Validate with
- 50. Waterfall BI/DW Limited Stakeholder interaction Analysis Design Development This Year BDUF Stakeholder Requirements Input Data Model Next Year Test Release ETL BI DATA VALUE?
- 51. Agile DW/BI Development Stakeholder interaction ? JEDUF BI Prototyping ETL Review Release This Year Next Year Iteration 1 VALUE? Iteration 2 ETL BI Iteration 3Rev ADM VALUE Iteration … VALUE! DATA Iteration n VALUE! VALUE!
- 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
- 54. BEAM✲ Methodology Structured, non-technical, collaborative working conversation directly with BI Users BEAM✲ BI User’s Business Process, Organizational, Hierarchical, and Data Knowledge • Focused Data Profiling • Data Modeler BI Stakeholders • Logical and Physical (Kimball-esque) Dimensional Data Models • Example data • Detailed and Testable ETL Specification • Instantiated DW Prototype
- 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
- 60. CALENDAR PRODUCT Date Key Product Key Date Day Day in Week Day in Month Day in Qtr Day in Year Month Qtr Year Weekday Flag Holiday Flag Product Code Product Description Product Type Brand Subcategory Category SALES FACT Date Key Product Key Store Key Promotion Key Quantity Sold Revenue Cost Basket Count STORE PROMOTION Store Key Promotion Key Store Code Store Name URL Store Manager Region Country Promotion Code Promotion Name Promotion Type Discount Type Ad Type
- 61. Customer Type Holiday Type Month Country Calendar Customer Sales Fact Store Product City Category Store Type Product Type
- 65. Collaborative / Conversational Design Who does what? “Customers buy products” BEAM✲ Modeler Subjects Verb Objects BI Users
- 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 Event Table Name (filled in later) Subject Column Name Verb Object Column Name Interrogative Details Example Data (4-6 rows)
- 68. Straightforward Methodology 1 1 1 1 1 1 Subject-Verb-Object 1 1 3 1 1 1 Who 1 1 4 1 1 1 What 1 1 5 1 1 1 When 1 1 2 1 1 1 Declare Event Type Where 1 1 6 1 1 1 How (many) Why Sufficient Detail Fact Granularity 1 1 7 1 1 1 1 1 8 1 1 1 How 1 1 9 1 1 1 Initial Data Examples Quantities - Facts
- 69. Capture Example Data 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 Old, Low Old, Low Value Oldest needed Near Min, Negative, 0 New, High New, High Most Recent, Future Far Max, Precision Multi-Level Engage business users Clarify definitions / Conform Dimensions Illustrate exceptions Drive out uniqueness “Show and tell” Multiple Values Exceptional Exceptional
- 70. Thoughtful Example Data Detailed ETL Specification
- 71. Identify Event Type Early
- 72. Adjust Conversation Based on Event Type • Discrete Event - Transaction • • Recurring Event - Periodic Snapshot – measurement • • Instantaneous/short duration, irregularly occurring events or transactions 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 “On the Order Date” BI Users
- 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
- 79. Annotate w Targeted Data Profiling
- 80. Proceed Through the Business Process Value Chain
- 86. Prototype! Not “Data Model Review”
- 87. Recap • Collaborative and Agile • • Data Sourcing • • Data Modeling 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 !