Agile BI/DW - Aalborg 2009

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    Agile BI/DW - Aalborg 2009 - Presentation Transcript

    1. Agile BI/DW Daniel Fagerström [email_address]
    2. Experience
      • Used agile methods 10 years
      • Developed BI/DW 5 years
      • Led large BI/DW project during last year using Scrum
      • Database lead at alaTest.com
    3. Why agile?
      • Deliver early
      • Reduce risk
      • Make the project steerable and transparent
      • Make development efficient and fun
    4. How?
    5. Just follow the book?
      • Yes, but …
      • What are the user stories?
      • Agile DB practices?
      • Agile operations?
    6. Overview
      • Before starting
      • Project cycles
      • Agile planning
      • Agile DB practices
      • Agile operations
    7. Before the project
      • Introduce agile methods while selling
      • Agile architecture, dimension models
      • Agile estimation based offering
    8. Sprint 0
      • Collocation (set up team room)
      • Appropriate HW, SW, test server
      • Involve operations
      • Contact the data source owners
    9. Cycles
      • Release cycle
        • Envision & fund
        • Plan
        • Build
        • Deploy & review
      • Sprint
        • Plan
        • Develop
        • Demo
        • Review
      • Daily Scrum
      Sprint Daily Scrum Sprint Planning Sprint Demo Sprint Retrospective Release cycle
    10. Planning
      • DW for loyalty card company
      • Epic
        • As marketing manager I want to use the DW to understand customer behavior
      • Theme
        • As a campaign manager I want to follow sale over time for a certain store
      • User story
        • I want the customer dimension to be updated daily
    11. Planning (decomposition)
      • Architecture driven story decomposition
        • See Agile Data Warehousing, Ralph Huges
      • Use course architecture as common language for Product owner and developers
      • Architectural dimensions
        • Subject area, Target layer, Transformation, type, Refresh type, Refresh frequency, Automation, User friendliness
      • Each user story must enable feedback
    12. Dimension model Customer name city Store name region Product name class Date year month ReceiptLine amount
    13. Planning (decomposition)
      • Start with the most important
        • Star (subject area)
        • Dimension
        • Attributes in the dimension
    14. Architecture Customer registry Product registry Org. registry Transactions Raw data Staging Dimension model Marts OLAP Cube Reporting Applications ETL
    15. Planning (decomposition)
      • ETL – Start with simpler/earlier tasks
        • Transformation type
          • Direct data -> aggregations -> business rules
        • Refresh frequency
          • One time load -> monthly -> weekly -> daily
        • Target layer
          • Raw -> staging -> presentation -> end user view
        • Refresh type
          • Link -> snapshot -> incremental
    16. Planning (decomposition)
      • BI
        • Transformation type
        • Refresh frequency
        • Automation
          • On demand on work station -> on demand posted on server -> scheduled
        • User friendliness
          • Single table -> linked table -> navigation -> pick list -> dash board
          • Using expert analysis tools -> reports
    17. Breadth or depth?
      • Breadth
        • Simplified load of the most important dimension models
        • Early feedback, earlier build of dependent systems
      • Depth
        • Complete load of one dimension at a time
        • Early deployment of usable sub systems
    18. Agile DB Practices
      • Agile DB refactoring
        • Scott Ambler
        • Change scripts for everything
      • Common code ownership
        • Everybody can can change the models
        • DB models in visual modeling tool
      • Don't become a bottleneck
        • Broad contact area between team members and users, operations and source data specialists
    19. Agile DB Practices
      • Course architecture
      • Agile modeling
      • Just in time design
      • Agile documentation
    20. Agile DB Practices
      • DB Schema in repo
      • Subset data
      • Your own DB and development sandbox
      • Staged deployment: dev -> test -> prod
      • Continuous deployment
      • TDD?
    21. Agile Operations
      • Involve operations from day one
      • Operations part of the project
      • Deploy at demo/production server often
      • Start source data deliveries ASAP
    22. Agile Operations
        -> Operations, support and maintenance during the project
      • Kanban for that?
      Project time #Features in prod.
    23. Conclusions
      • Some mental barriers for DB/DW/BI-developers
      • Transparent
      • Steerable
      • Works great!
      • Happy customers (and developers)!
    24. Literature
    25. www.danielfagerstrom.com
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