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DATA WAREHOUSINGMulti DimensionalData Modeling.DW Bus architecture and matrix
2
   Obviously, building the enterprise’s data    warehouse in one step is too daunting, yet    building it as isolated pie...
   By defining a standard bus    interface for the data    warehouse environment,    separate data marts can be    implem...
   During the limited duration architecture phase, the    team designs a master suite of standardized    dimensions and f...
   The rows of the bus matrix    correspond to data marts.   You should create separate    matrix rows if the sources   ...
   Common dimensions for different processes    should be the same.     A dimension that has exactly the same meaning   ...
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   Revenue, profit, standard prices, standard    costs, measures of quality, measures of    customer satisfaction, and ot...
   The Data Warehouse Toolkit.Second    Edition.The Complete Guide to Dimensional    Modeling.Ralph Kimball.Margy Ross   ...
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Dw design 4_bus_architecture

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Dw design 4_bus_architecture

  1. 1. DATA WAREHOUSINGMulti DimensionalData Modeling.DW Bus architecture and matrix
  2. 2. 2
  3. 3.  Obviously, building the enterprise’s data warehouse in one step is too daunting, yet building it as isolated pieces defeats the overriding goal of consistency. For long-term data warehouse success, we need to use an architected, incremental approach to build the enterprise’s warehouse. The approach we advocate is the data warehouse bus architecture. 3
  4. 4.  By defining a standard bus interface for the data warehouse environment, separate data marts can be implemented by different groups at different times. The separate data marts can be plugged together and usefully coexist if they adhere to the standard 4
  5. 5.  During the limited duration architecture phase, the team designs a master suite of standardized dimensions and facts that have uniform interpretation across the enterprise. This establishes the data architecture framework 5
  6. 6.  The rows of the bus matrix correspond to data marts. You should create separate matrix rows if the sources are different, the processes are different, or if the matrix row represents more than what can reasonably be tackled in a single implementation iteration. 6
  7. 7.  Common dimensions for different processes should be the same.  A dimension that has exactly the same meaning and content when being referred from different fact tables.  Where attributes apply, they should mean the same thing.  Roll-up dimensions conform to the base-level atomic dimension if they are a strict subset of that atomic dimension. (granularity) 7
  8. 8. 8
  9. 9.  Revenue, profit, standard prices, standard costs, measures of quality, measures of customer satisfaction, and other key performance indicators (KPIs) are facts that must be conformed. 9
  10. 10.  The Data Warehouse Toolkit.Second Edition.The Complete Guide to Dimensional Modeling.Ralph Kimball.Margy Ross 10

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