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Functionality of Data Warehouses and Building a Data Warehouse

Data warehouses exist to facilitate complex, data-intensive, and frequent adhoc queries. The data warehouse access component supports enhanced spreadsheet functionality, efficient query processing, structured and adhoc queries, data mining, and materialized views. These offer pre-programmed functionalities such as :
– Roll-up : Data is summarized with increasing generalization.
– Drill-down : Increasing levels of details are revealed.
– Pivot : Cross tabulation is performed.
– Slice and dice : Performing projection operations on the dimensions.
– Sorting : Data is sorted by ordinal value.
– Selection : Data is available by value or range.
– Derived attributes : Attributes are computed by operations on stored and derived values.

BUILDING A DATA WAREHOUSE :
In constructing a data warehouse, builders should take a broad view of the anticipated use of the data warehouse. Acquisition of data for the warehouse involves the following steps :
– The data must be extracted from different sources.
– Data must be formatted for consistency within the data warehouse. Names, meanings, and domains of data from unrelated sources must be reconciled.
– Data must be cleaned to ensure validity.
– The data must be fitted into the data model of the data warehouse.
– The data must be loaded into the warehouse.
– How up-to-date must be data be ?
* Can the warehouse go off-line, and for how long.
* What are the data interdependencies ?
* What is the storage availability ?
* What are the distribution requirements ?
* What is the loading time ?
Data warehouses must also be designed with full consideration of the environment in which they will reside. Important design considerations include the following :
– Usage projections.
– The fit of the data model.
– Characteristics of available sources.
– Design of the metadata component.
– Modular component design.
– Design for manageability and change.
– Consideration of distributed and parallel architecture.

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