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IS6120 Owen Devitt 100000523. Data Warehouse (Corporate Information Factory) “You can catch all the minnows in the ocean and stack them together and they still do not make a whale.” Bill Inmon. Data Mart (Data Warehouse Bus)

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IS6120 Owen Devitt 100000523

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IS6120

Owen Devitt

100000523


Data Warehouse

(Corporate Information Factory)

“You can catch all the minnows in the ocean and stack them together and they still do not make a whale.”

  • Bill Inmon

Data Mart

(Data Warehouse Bus)

“… The data warehouse is nothing more than the union of all the data marts …”

  • Ralph Kimball


Two Types of Data Mart

  • Dependent Marts

  • Independent Marts

Legacy Systems


Data Warehouse

  • Top-Down approach

  • Holds multiple subject data

  • Services the needs of all users – owned by corporation

  • Low-level granularity

  • Normalized

  • Useful for data mining – discovering previously unknown connections


Data Warehouse

  • Criticism

  • Kimball and other data mart vendors suggest that DWs are large, long-term projects and that value is produced only after a number of years

    • Inmon refutes this claim (Inmon, 1999)

  • Expensive to maintain

  • Slow deployment


  • Data Warehouse Development

    • Iterative development


    Data Mart

    • Bottom-up approach

    • Owned by a department

    • Services the needs of specific business units/departments

    • Star-join structure

    • Technology optimal for access and analysis

    • Rapid Deployment

    • “…departments and divisions are going to create their own mini data warehouses to answer urgent business questions…” (Kimball, 1998)


    Data Mart

    • Criticism

    • Inmon suggests that data mart granularity is not as low-level as data warehouse granularity

      • Kimball refutes this claim (Kimball, 1998)

  • Large amounts of redundancy

  • High-level granularity (according to Inmon)

  • Synchronicity an issue

  • Large numbers of data marts become as difficult as legacy systems to integrate


  • Data Mart Development

    • Independent development


    Similarities

    • Both use a staging area

      • Data Warehouse (DW view)

      • Backroom (DM view)

  • Both extract from a single source once

  • Both claim to be based on the most atomic data available from the source


  • Key Differences

    Data Warehouse

    • Hard work is done at the beginning

    • Dependent Data Marts – sourced from the DW

    • Iterative development

    Data Mart

    • Hard work is done on integration

    • Independent Data Marts – sourced from the legacy systems

    • Independent development


    Conclusion

    • A Data Warehouse may be equal to the sum of its dependent Data Marts

    • Data Marts are useful for organizations that do not intend to utilize a corporate-wide data warehouse

    • Data Warehouse architecture is more robust and scalable than Data Mart architecture

    • Only the strictest, most forward-thinking data mart development can be equivalent to a data warehouse


    Conclusion


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