Data warehouses struggle with combining too much standardized information.
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Q12: Within the data warehouse model, the external
Q24: The primary purpose of a data warehouse
Q27: The complete removal of dirty data from
Q29: A data mart contains a subset of
Q31: One example of a data aggregation is
Q35: According to Gartner Inc., dirty data is
Q37: Data aggregation is the collection of data
Q51: Duplicate data, inaccurate data, misleading data, and
Q52: Dirty data is erroneous or flawed data.
Q53: ETL stands for extraction, transformation, and loading.
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