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.

Summary


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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