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by Burleson Consulting

The Data Warehouse Development Life Cycle

Oracle Data Warehouse Design
De-normalization And Data Warehouse Sizing
Now, let’s apply these principles to the analysis we conducted on Guttbaum’s Grocery. The first step is to start with our 3NF model from Figure 4.1 and locate the “facts,” which are always numerical items at the bottom of the entity hierarchy. In our example, quantity and sale_amount in the transaction entity are the facts. Because these appear in the TRANSACTION table in our 3NF model for Guttbaum’s Grocery, we can presume that the transaction table will form the basis for our fact table for our data warehouse.

However, here we return to an important point regarding data aggregation. Our 3NF model for Guttbaum’s assumes that there will be one row for each item on each and every transaction. Because Guttbaum’s Grocery chain has 50 supermarkets, and an average of 3,000 customers each day for each supermarket, with an average of 10 items per transaction, we see that 1.5 million rows are added to Guttbaum’s database each day. Assuming that each transaction row is 20 bytes, we need 30 MBs of disk space per day just to hold the transactions, which translates into 30,000,000 X 365 days = 1,100,000,000,000 bytes, or 1.1 terabytes per year! Even with the price of disk running at the relatively inexpensive rate of $100 per gigabyte or $10,00 per terabyte, we may decide that the cost of disk storage is prohibitively expensive to store this level of transaction detail. One alternative to keeping the lowest level of transaction detail would be to summarize the transaction data, either by total per customer transaction or total by product category. Of course, we may lose some information in this summarization, but we would save a substantial amount of disk storage expense. For details on the issues involved with data aggregation and summarization, refer to Chapter 10, Oracle Data Warehouse Utilities.

So, even if we can keep our transaction table rows to 20 bytes, the additional disk cost for adding another redundant data item to the fact table is rather substantial. Each additional 20 bytes of data will cost Guttbaum’s $10,000 per year in additional disk expense, so we must carefully consider which redundant data items will appear in the fact table and which data items will reside in the adjacent tables of our STAR schema design. Remember the tradeoff--we are sacrificing disk costs for speed of data retrieval. Each attribute added to the fact table reduces the number of table joins that our Oracle warehouse must incur to service a transaction. Hence, only frequently referenced data items are candidates for inclusion in out fact table.

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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