What are the methodologies of Data Warehousing.

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sangroover

  • Feb 15th, 2006
 

Every company has methodology of their own. But to name a few SDLC Methodology, AIM methodology are stardadly used. Other methodologies are AMM, World class methodology and many more.

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Sanyam Mittal

  • Feb 26th, 2006
 

Most of the time ,we use Mr. Ralph Kimball methodologies for datawarehousing design.Two kind of schema :star and snow flake.

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ravi kumar guturi

  • Mar 9th, 2006
 

Most probabally Every one fallows

Either starschema or snowflake schema

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raja

  • Mar 9th, 2006
 

hello sir,

      i am raja, i am doing my final year,B.Tech (I.T),  i am doing my final sem project in data warehouse, title is "INVENTORY SYSTEM". in our college documentation  we use one procedure, so i need the methodologies, now i am using the water fall model in SDLC. is it correct or not.  only use the schema is the methodologies for dwh.

    please help me sir.

  

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sunilreddy

  • Mar 17th, 2006
 

there r 2 methodologies 1)kimball-first datamarts then EDWH 2)inmon-first EDWH then datamarts from edwh

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SVS RAJESH

  • Mar 20th, 2006
 

   Regarding the methodologies in the Datawarehousing . They are mainly 2 methods.

  1. Ralph Kimbell Model

  2. Inmon Model.

   Kimbell model always structed as Denormalised structure.

   Inmon model structed as Normalised structure.

    Depends on the requirements of the company anyone can follow the company's DWH will choose the one of the above models.

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satyambabu

  • Mar 22nd, 2006
 

In Data warehousing contains the Two Methods

1>> Top Down Method

2>>Bottom up method

In Top Down method First load the Datamarts and then load the dataware house.

In Bottom Up method first load the Data warehouse and then load the Datamarts.

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Srinu

  • Mar 29th, 2006
 

Top Down approach is first Data warehouse then Datamarts.

Bottom Down approach is first Datamarts then Data warehouse.

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Ravi

  • Apr 2nd, 2006
 

Yes

There are 2 methodalogies 1. Kimball 2. Inmon likewise                               1. Star Flake 2. Snow Flake schemas

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Raghunath Bhairavajosula

  • May 12th, 2006
 

There are two appoaches in Dataware housing named as

Top Down Approach and Bottom-up Approach

Top down approach in the sense preparing individual departments data(DataMarts) from the Enterprise DatawareHouse

Bottom Up Approach is nothing but first gathering all the departments data and then cleanse the data and Transofrms the data and then load all the indivisual departments data into the enterprise data ware house

venkat

  • May 13th, 2006
 

Hi ,

DWH porject should ideally use the Spiral Model because its the iterativ approach.

we build one data mart,test and go to the user and after that we again take the scratch for the next data mart

we do this untill all the data marts are completed so we can defnetly say its a spiral model and not waterfall model

All th best for ur project

Venkat

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Loveojha

  • May 17th, 2006
 

There can be actually three methodology as suggested by Ralph Kimball

1. Top Down Approach (Build using a Big Bang Approach, which takes time and result won't be visible very quickly, but will be more consistent across.) First build the DW and then the Data Marts.

2. Bottom Up Approach(Build smaller piece of blocks(marts) which are too a certain extent independent of others, results are visible very quickly, but risk of non-consistency and redundancy is always there) First the Data Marts then the DW.

3. Hybrid Approach(Perhaps a bit practical, first plan and work for the standards to be followed for the consistency and conformedness, then Build the individual Data Marts which follow the defined standards, avoids the risk of redundancy & non-consistency, relatively faster then the top down)Mix of both top down and bottom up

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Kalai

  • Jul 29th, 2006
 

There are four methods in which one can build a datawarehouse.

1. Top-Down (Emphasizes the DW. )

2. Bottom-Up (Emphasizes data marts.)

3. Hybrid (Emphasizes DW and data marts; blends ?top-down? and ?bottom-up? methods.)

4. Federated (Emphasizes the need to integrate new and existing heterogeneous BI environments.)

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