Kimball Data: Modeling Data as Both Fact and Dimension with ViewDatawarehouse Design: Combined Date Time...

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Kimball Data: Modeling Data as Both Fact and Dimension with View


Datawarehouse Design: Combined Date Time dimension vs. Separate Day and Time dimensions and timezonesData modeling membership and profileshow many fact tables do I need given I want to build an OLAP for Quotation Line Item and Purchase Order Line Item report?Time dimension or timestamp in fact table?Non numeric attributes in fact table (to track data source)?Data Warehouse Design and Double DippingWhen to Converge Dimensions in a Data Warehouse When There Are Few Common AttributesData Warehouse vs Data Mart vs database (separating logic from hardware)Is the modeling technique changing with column-oriented databases?Data Warehouse: Can a Transaction Table also be a Dimension?













0















In Data warehousing, Kimball discusses modeling an insurance policy premium as Both Dimension and Fact.
I have the following table,



create table dbo.DimAutoInsurance
(
DimAutoInsuranceId int primary key identity(1,1),
CustomerName varchar(100),
CustomerAddress varchar(255),
PolicyCoverageAmount numeric (15,2),
PolicyBeginDate datetime,
PolicyExpirationDate datetime
)


For the Fact table, should I reconduct ETL another table for fact? Copying the data again, would seem redundant.
Or should I create a view? What is best database design strategy?



create view dbo.FactAutoInsurance
as
select
DimAutoInsuranceId,
PolicyCoverageAmount numeric (10,2),
from dbo.DimAutoInsurance


https://www.kimballgroup.com/2007/12/design-tip-97-modeling-data-as-both-a-fact-and-dimension-attribute/



Kimball:
A more ambiguous example is the limit on a coverage within an automobile insurance policy. The limit is a numerical data item, say $300,000 for collision liability. Furthermore, many queries would group or constrain on this limit data item. This sounds like a slam dunk for the limit being an attribute of the coverage dimension.



One could pose some important queries summing or averaging all the limits on many policies and coverages. This sounds like a slam dunk for the limit being a numeric fact in a fact table.
Rather than agonizing over the dimension versus fact choice, simply model it BOTH ways! Include the limit in the coverage dimension so that it participates in the usual way as a target for constraints and the content for row headers, but also put the limit in the fact table so it can participate in the usual way within complex computations."









share



























    0















    In Data warehousing, Kimball discusses modeling an insurance policy premium as Both Dimension and Fact.
    I have the following table,



    create table dbo.DimAutoInsurance
    (
    DimAutoInsuranceId int primary key identity(1,1),
    CustomerName varchar(100),
    CustomerAddress varchar(255),
    PolicyCoverageAmount numeric (15,2),
    PolicyBeginDate datetime,
    PolicyExpirationDate datetime
    )


    For the Fact table, should I reconduct ETL another table for fact? Copying the data again, would seem redundant.
    Or should I create a view? What is best database design strategy?



    create view dbo.FactAutoInsurance
    as
    select
    DimAutoInsuranceId,
    PolicyCoverageAmount numeric (10,2),
    from dbo.DimAutoInsurance


    https://www.kimballgroup.com/2007/12/design-tip-97-modeling-data-as-both-a-fact-and-dimension-attribute/



    Kimball:
    A more ambiguous example is the limit on a coverage within an automobile insurance policy. The limit is a numerical data item, say $300,000 for collision liability. Furthermore, many queries would group or constrain on this limit data item. This sounds like a slam dunk for the limit being an attribute of the coverage dimension.



    One could pose some important queries summing or averaging all the limits on many policies and coverages. This sounds like a slam dunk for the limit being a numeric fact in a fact table.
    Rather than agonizing over the dimension versus fact choice, simply model it BOTH ways! Include the limit in the coverage dimension so that it participates in the usual way as a target for constraints and the content for row headers, but also put the limit in the fact table so it can participate in the usual way within complex computations."









    share

























      0












      0








      0








      In Data warehousing, Kimball discusses modeling an insurance policy premium as Both Dimension and Fact.
      I have the following table,



      create table dbo.DimAutoInsurance
      (
      DimAutoInsuranceId int primary key identity(1,1),
      CustomerName varchar(100),
      CustomerAddress varchar(255),
      PolicyCoverageAmount numeric (15,2),
      PolicyBeginDate datetime,
      PolicyExpirationDate datetime
      )


      For the Fact table, should I reconduct ETL another table for fact? Copying the data again, would seem redundant.
      Or should I create a view? What is best database design strategy?



      create view dbo.FactAutoInsurance
      as
      select
      DimAutoInsuranceId,
      PolicyCoverageAmount numeric (10,2),
      from dbo.DimAutoInsurance


      https://www.kimballgroup.com/2007/12/design-tip-97-modeling-data-as-both-a-fact-and-dimension-attribute/



      Kimball:
      A more ambiguous example is the limit on a coverage within an automobile insurance policy. The limit is a numerical data item, say $300,000 for collision liability. Furthermore, many queries would group or constrain on this limit data item. This sounds like a slam dunk for the limit being an attribute of the coverage dimension.



      One could pose some important queries summing or averaging all the limits on many policies and coverages. This sounds like a slam dunk for the limit being a numeric fact in a fact table.
      Rather than agonizing over the dimension versus fact choice, simply model it BOTH ways! Include the limit in the coverage dimension so that it participates in the usual way as a target for constraints and the content for row headers, but also put the limit in the fact table so it can participate in the usual way within complex computations."









      share














      In Data warehousing, Kimball discusses modeling an insurance policy premium as Both Dimension and Fact.
      I have the following table,



      create table dbo.DimAutoInsurance
      (
      DimAutoInsuranceId int primary key identity(1,1),
      CustomerName varchar(100),
      CustomerAddress varchar(255),
      PolicyCoverageAmount numeric (15,2),
      PolicyBeginDate datetime,
      PolicyExpirationDate datetime
      )


      For the Fact table, should I reconduct ETL another table for fact? Copying the data again, would seem redundant.
      Or should I create a view? What is best database design strategy?



      create view dbo.FactAutoInsurance
      as
      select
      DimAutoInsuranceId,
      PolicyCoverageAmount numeric (10,2),
      from dbo.DimAutoInsurance


      https://www.kimballgroup.com/2007/12/design-tip-97-modeling-data-as-both-a-fact-and-dimension-attribute/



      Kimball:
      A more ambiguous example is the limit on a coverage within an automobile insurance policy. The limit is a numerical data item, say $300,000 for collision liability. Furthermore, many queries would group or constrain on this limit data item. This sounds like a slam dunk for the limit being an attribute of the coverage dimension.



      One could pose some important queries summing or averaging all the limits on many policies and coverages. This sounds like a slam dunk for the limit being a numeric fact in a fact table.
      Rather than agonizing over the dimension versus fact choice, simply model it BOTH ways! Include the limit in the coverage dimension so that it participates in the usual way as a target for constraints and the content for row headers, but also put the limit in the fact table so it can participate in the usual way within complex computations."







      sql-server database-design data-warehouse sql-server-2017





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      Joe Smith 8435Joe Smith 8435

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