Building the Data Warehouse

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The data warehouse environment then is fundamentally different from the classical production environment because, under most circumstances, a test environment is simply not needed.


Some technological features are required for satisfactory data warehouse processing. These include a robust language interface, the support of compound keys and variable-length data, and the abilities to do the following:

■■ Manage large amounts of data.

■■ Manage data on a diverse media.

■    Easily index and monitor data.

■■ Interface with a wide number of technologies.

■    Allow the programmer to place the data directly on the physical device.

■    Store and access data in parallel.

■    Have meta data control of the warehouse.

■■ Efficiently load the warehouse.

■■ Efficiently use indexes.

■■ Store data in a compact way.

■■ Support compound keys.

■    Selectively turn off the lock manager.

■    Do index-only processing.

■    Quickly restore from bulk storage.

Additionally, the data architect must recognize the differences between a transaction-based DBMS and a data warehouse-based DBMS. A transaction-based DBMS focuses on the efficient execution of transactions and update. A data warehouse-based DBMS focuses on efficient query processing and the handling of a load and access workload.

Multidimensional OLAP technology is suited for data mart processing and not data warehouse processing. When the data mart approach is used as a basis for data warehousing, many problems become evident:

■    The number of extract programs grows large.

■    Each new multidimensional database must return to the legacy operational environment for its own data.

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