As the size of the database increases, redundancy can become a major problem. Today, many users create very large databases, many of which contain trillions of bytes. For databases of this size, a single table can contain more than a billion rows, and the introduction of a single new column to a table can represent thousands of dollars in additional disk expense. Data redundancy is detrimental for two reasons. First and foremost, duplicating redundant material consumes disk storage space. Second and more ominous, updating redundant data requires extra processing. Redundant duplication of very large and highly volatile data items can cause huge processing bottlenecks.

However, the overhead associated with data redundancy does not imply that redundancy is always undesirable. Performance is still an overriding factor in most systems. Proper control of redundant information implies that redundant information can be introduced into any structure as long as the performance improvements outweigh the additional disk costs and update problems.

Since the first publication of Dr. Codd's 1993 research paper Providing OLAP (Online Analytical Processing) to User-Analysts: An IT Mandate, database designers have attempted to find an optimum way of structuring tables for low data redundancy. Codd's rules of normalization guide the designer to create a logically correct table structure with no redundancy, but performance rules often dictate the introduction of duplicated data to improve performance.

This is especially true for Oracle data warehouses. However, the warehouse designer does not have free reign to introduce redundancy anywhere in the model. Redundancy always carries a price, whether it is the cost of the disk storage or the cost of maintaining a parallel update scheme. Figure 4.2 shows a strategy for analyzing the consequences of data redundancy.

Figure 4.2 A comparison of size versus volatility for redundant data.