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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

Owen Devitt


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
Two Types of Data Mart
  • Dependent Marts
  • Independent Marts

Legacy Systems

data warehouse
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 warehouse1
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
Data Warehouse Development
  • Iterative development
data mart
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 mart1
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
Data Mart Development
  • Independent development
  • 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
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
  • 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