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by Burleson Consulting

The Data Warehouse Development Life Cycle

Online Analytical Processing and Oracle
Simulation Of Cubic Databases (Dimensionality)

Keep in mind that dimensions may be hierarchical in nature, adding further confusion. A time dimension, for example, may be represented as a hierarchy with year, quarter, month, and day. Each of these levels in the dimension hierarchy may have its own values. In other words, a cubic representation with time as a dimension may be viewed in the following two ways:

* A series of cubes--one for year, another for quarter, and another for full_date.

* A five-dimension table.

MDDBs are most commonly used with data that is a natural fit for pivot tables, and it should come as no surprise that most MDDB sites are used with finance and marketing applications. Unfortunately, most multidimensional databases do not scale up well for warehouse applications. For example, the largest supported database for Essbase is about 20 GB, whereas data warehouses with sizes measured in terabytes are not uncommon.

It is important to note that defining aggregation of a multidimensional database is no different than defining aggregate tables for a relational database. At load time, the database will still need to compute the aggregate values. MDDBs also employ the concept of sparse data. Because data is aggregated and pre-sliced, some cells on a cube may not contain data. For example, consider a cube that tracks sales of items across a large company. The cells representing sales of thermal underwear would be null for Hawaii, and the sales of surfboards in Wyoming would also be null. Nearly all of the product offerings are able to maintain a mechanism for compressing out these types of null values.

This is an excerpt from "High Performance Data Warehousing". To learn more about Oracle, try "Oracle Tuning: The Definitive Reference", by Donald K. Burleson.  You can buy it direct from the publisher at 30% off here:


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