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The Death of the Data Warehouse

The Death of the Data Warehouse. Michigan Oracle User Summit 14 November 2012. The Business Problems We’re Trying to Solve w/ DW & BI?. Business people can’t get to their data Running summary reports out of transaction databases is very slow P erformance issues of transaction DB

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The Death of the Data Warehouse

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  1. The Death of the Data Warehouse Michigan Oracle User Summit 14 November 2012

  2. The Business Problems We’re Trying to Solve w/ DW & BI? • Business people can’t get to their data • Running summary reports out of transaction databases is very slow • Performance issues of transaction DB • Reporting is complex • Disparate databases - No integrated view of the whole company • Transaction systems discard history

  3. What we need to solve these • Subject Oriented • Integrated • Time variant • Non volatile

  4. What we create

  5. The Traditional DW Model • Complexities • Technologies to master • Data modeling • ETL • BI • DBA • Workplan steps to complete • Design data mart databases • Design DW databases • Design BI tool metadata • Build flows from source systems to DW • Build flows from DW to data marts • Build BI metadata • Result • Time consuming • Brittle (e.g. change to one column in the source ripples through architecture)

  6. Traditional BI Development Success? $

  7. Data Warehouse Definition – The Physical DB Implications • Subject Oriented • Integrated • Time variant • Non volatile This is a LOGICAL definition, not a physical one – it says nothing about how the data must be stored or accessed

  8. New Generation of BI Tools (QlikView, Tableau, etc.) • They contain their own, non-relational, self-managing data stores. • They can import data from multiple sources into a single, accessible data store. • They join related data together, like a relational database. • They provide predictable, blisteringly fast query performance • They provide very easy, user-friendly user interfaces. • They can contain, and rapidly summarize, atomic-level, granular data. • They can be incrementally refreshed, enabling the storage of history. These tools meet the definition of a data warehouse but are far more efficient

  9. The Traditional DW Model TRADITIONAL • Complexities • Technologies to master • Data modeling • ETL • BI • DBA • Workplan steps to complete • Design data mart databases • Design DW databases • Design BI tool metadata • Build flows from source systems to DW • Build flows from DW to data marts • Build BI metadata • Result • Time consuming • Brittle (e.g. change to one column in the source ripples through architecture) NEW WORLD • Complexities • Technologies to master • In memory tool • Workplan steps to complete • Build flows from source systems to DW • Build reports • Result • Agile • Easily revised

  10. Preferred Model of BI Development User Input Dev & Rvw Yes User Input Dev & Rvw Yes User Input Dev & Rvw No No Quit Quit Develop DW in Parallel with Input from BI (If Necessary) $

  11. In Memory Advantages & Disadvantages

  12. Questions?

  13. Traditional BIArchitecture (e.g. Cognos Rpt Studio) Source DB 1 Point & click to generate SQL Database –Operational or Informational Source DB 2 Format presentation

  14. QlikView Architecture Point & click to generate Query Associative DB Source DB 1 Data Warehouse Format presentation

  15. Demo

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