Data Warehousing

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  • Topic: Data warehousing, Data warehouse, Bill Inmon
  • Pages : 2 (331 words )
  • Download(s) : 69
  • Published : July 17, 2010
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Case Study 2 1.) Compare and contrast Inmon and Kimball’s definition of Data Warehousing.

Bill Inmon advocates a top-down development approach that adapts traditional relational database tools to the development needs of an enterprise wide data warehouse. From this enterprise wide data store, individual departmental databases are developed to serve most decision support needs.

Ralph Kimball, on the other hand, suggests a bottom-up approach that uses dimensional modeling, a data modeling approach unique to data warehousing. Rather than building a single enterprise wide database, Kimball suggests creating one database or data mart per major business process. 2.) Which of the two ideas do you think is suitable and practical to be used in the industry and why? I think that Kimball’s model is suitable and practical in the industry for the following reasons: First is that in terms of complexity of the method, Kimball’s approach is fairly simple compare to Inmon which is quite complex. Kimball uses the four-step process, a departure from RDBMS methods. In terms of data modeling, the approach used by Kimball is process oriented and it has high end-user accessibility compared to Inmon. Kimball’s primary audience is the end users and that it delivers a solution that makes it easy for the end user to directly query the data and still get reasonable response times. This makes me think that Kimball’s model is suitable for use in the industry.

3.) Data Warehouse implementation using Northwind Database. Sales Star Schema

1. Extract Data From Multiple Sources 2. Integrate, Transform, and Restructure Data 3. Load Data Into Dimension Tables and Fact Tables Verifying Accuracy of Source Data  Integrating data from multiple sources  Applying business rules  Checking structural requirements Correcting Invalid Data  Transforming data  Reassigning data values Managing Invalid Data  Rejecting invalid data  Saving invalid data to a log Verifying the Fact Table Source...
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