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Data Vault ModelingWHAT’S NEXT??? © Dan Linstedt 2009-2012 This was a presentation I gave at an Array Conference In the Netherlands, in 2009.
A bit about me… 2 Author, Inventor, Speaker – and part time photographer… 25+ years in the IT industry Worked in DoD, US Gov’t, Fortune 50, and so on… Find out more about the Data Vault: http://www.youtube.com/LearnDataVault http://LearnDataVault.com Full profile on http://www.LinkedIn.com/dlinstedt 10/5/2011 LearnDataVault.com
Who’s Using It? 10/5/2011 LearnDataVault.com 3
Featured Word from Our Customer 10/5/2011 LearnDataVault.com 4 Jonathan Rice, BSc ING Real Estate/Development International/Operational Support  Location HP A.05.071 PO Box 90463, 2509 LL The Hague, The Netherlands They are connecting MS SharePoint back-end to Data Vault Modeling EDW.
What’s Changing the Game? Solid State Disk (SSD) Hosted Cloud Computing Column Based Databases Unstructured Information Ontologies / Taxonomies Mining Engines Broad Based Web Services Business Demand for Immediate Answers (pushing real-time) Compliance and Auditability (pushing volume) 10/5/2011 LearnDataVault.com 5 ,[object Object]
Flash & Silverlight Front-Ends
Analytic functions in Database Engines
Business Rules Engines Melding with ETL and Web Services
DW Appliances,[object Object]
First Change: Business Data Vault 10/5/2011 LearnDataVault.com 7 SOA Enterprise BI Solution Star Schemas (real-time) Sales Batch DV EDW BDV EDW Staging Error Marts Finance Contracts Report Collections Business Rules Downstream! (the Lens Filter)
Next Change: Staging Removal 10/5/2011 LearnDataVault.com 8 Enterprise BI Solution SOA Write Back (real-time) Star Schemas Sales DV EDW BDV EDW Error Marts Finance Contracts Report Collections Business Rules Downstream! (the Lens Filter)
Next Change: Virtual Marts 10/5/2011 LearnDataVault.com 9 Enterprise BI Solution SOA Write Back Virtual  Marts & Dynamic Cubes (real-time) Sales Finance DV EDW BDV EDW Contracts Business Rules Downstream! (the Lens Filter)
Next Change: Unstructured Data 10/5/2011 LearnDataVault.com 10 Unstructured Data Sets Ontologies/Taxonomies Unstructured  Processing Engine ,[object Object]
Docs
Images
Movies
SoundOn-Demand Cubes Joins through LINK Structures Raw Data Vault EDW
A Look at Ontologies 10/5/2011 LearnDataVault.com 11 Hierarchies of Data: Synonyms, Homonyms, Antonyms, Related Terms, Definitions, Categorizations, Organizations, Views of the data world Ontologies HOLD THE KEY to understanding/conceptual relevance Ontologies can PIVOT raw data in to many different results

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Data vault: What's Next

  • 1. Data Vault ModelingWHAT’S NEXT??? © Dan Linstedt 2009-2012 This was a presentation I gave at an Array Conference In the Netherlands, in 2009.
  • 2. A bit about me… 2 Author, Inventor, Speaker – and part time photographer… 25+ years in the IT industry Worked in DoD, US Gov’t, Fortune 50, and so on… Find out more about the Data Vault: http://www.youtube.com/LearnDataVault http://LearnDataVault.com Full profile on http://www.LinkedIn.com/dlinstedt 10/5/2011 LearnDataVault.com
  • 3. Who’s Using It? 10/5/2011 LearnDataVault.com 3
  • 4. Featured Word from Our Customer 10/5/2011 LearnDataVault.com 4 Jonathan Rice, BSc ING Real Estate/Development International/Operational Support Location HP A.05.071 PO Box 90463, 2509 LL The Hague, The Netherlands They are connecting MS SharePoint back-end to Data Vault Modeling EDW.
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  • 7. Analytic functions in Database Engines
  • 8. Business Rules Engines Melding with ETL and Web Services
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  • 10. First Change: Business Data Vault 10/5/2011 LearnDataVault.com 7 SOA Enterprise BI Solution Star Schemas (real-time) Sales Batch DV EDW BDV EDW Staging Error Marts Finance Contracts Report Collections Business Rules Downstream! (the Lens Filter)
  • 11. Next Change: Staging Removal 10/5/2011 LearnDataVault.com 8 Enterprise BI Solution SOA Write Back (real-time) Star Schemas Sales DV EDW BDV EDW Error Marts Finance Contracts Report Collections Business Rules Downstream! (the Lens Filter)
  • 12. Next Change: Virtual Marts 10/5/2011 LearnDataVault.com 9 Enterprise BI Solution SOA Write Back Virtual Marts & Dynamic Cubes (real-time) Sales Finance DV EDW BDV EDW Contracts Business Rules Downstream! (the Lens Filter)
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  • 14. Docs
  • 17. SoundOn-Demand Cubes Joins through LINK Structures Raw Data Vault EDW
  • 18. A Look at Ontologies 10/5/2011 LearnDataVault.com 11 Hierarchies of Data: Synonyms, Homonyms, Antonyms, Related Terms, Definitions, Categorizations, Organizations, Views of the data world Ontologies HOLD THE KEY to understanding/conceptual relevance Ontologies can PIVOT raw data in to many different results
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  • 28. Why go Operational Data Vault? Benefits Include Direct access to data Removal of 90% of batch streams, replaced by Transactions Better/faster alerting capabilities Direct control over changes to BI answer sets What are the risks? The Data Vault EDW becomes an ODV, brings with it ALL responsibilities of the Operational System What are the problems? Row Locking Consistent Data Access Data Version Control Security of Data through access points Has it been done before? Yes, Cendant Timeshare Resource Group did it in 2002! 10/5/2011 LearnDataVault.com 14
  • 29. Plateau: Dynamic Data Warehouse 10/5/2011 LearnDataVault.com 15 Dynamically Constructed Links Dynamically Created Hubs Dynamically Altered Satellites Dynamically changed ETL/ELT Dynamically Altered Queries Automatically Updated Cubes = Self Morphing (guided) Data Vault H L DL H L Meta Mining Engine H L H H DL L H
  • 30. Impacts of Dynamic Data Vaults Business New found knowledge Evolving Data Warehouse models over time Faster Reporting Self-Maintaining Back-Ends Technical Guided Changes to Structures Auto-Adapting Load Routines Auto-Adapting Query Sets Auto-Suggested Star Schemas 10/5/2011 LearnDataVault.com 16
  • 31. Why go Dynamic? Benefits include: Faster Turn Around Time Auto Configuration of “mundane tasks” Discovery of new relationships (could result in increased revenue) Self Healing Structures How Long before we See It? 5 to 7 years out How do we get there? Meta-Mining Use of Ontologies inside the EDW What’s driving us there? Business Users want faster turn around time Business Users want ingestion of Unstructured Data Sets Business Users need more control over their systems 10/5/2011 LearnDataVault.com 17
  • 32. Where To Learn More The Technical Modeling Book: http://LearnDataVault.com The Discussion Forums: & eventshttp://LinkedIn.com – Data Vault Discussions Contact me:http://DanLinstedt.com - web siteDanLinstedt@gmail.com - email World wide User Group (Free)http://dvusergroup.com 18 10/5/2011 LearnDataVault.com