Data Warehouse Source Document Input Oracle Tips by Burleson Consulting

The Data Warehouse Development Life Cycle

ANALYZING SOURCE DOCUMENT INPUT

GUTTBAUM’S GROCERY STORES

For many future examples in this text, we will be using a fictional grocery store chain called Guttbaum’s Grocery. Guttbaum’s has been using computers to record sales for about 10 years but has only recently decided to put their transaction data online. As we begin our review of Guttbaum’s data system, we see that transactions are captured at the point-of-sales and summarized on a monthly basis for the OLTP system and individuals. Transactions are archived using tape storage. The use of tape storage was chosen because it was deemed to be the most efficient method for storing the massive quantities of data captured by Guttbaum’s system.
Inside Guttbaum’s data structures, one row of the transaction table is used for each purchased item within a grocery purchase. For example, if a customer checks out with a grocery cart holding 100 items, 100 rows are added to the fact table at the time the transaction is completed. Guttbaum’s Grocery chain has 50 supermarkets with an average of 3,000 customers each day per supermarket. Each customer averages 10 items per transaction. If we do the math, we can see that 1,500,000 rows are added to Guttbaum’s database each day. 1.5 million rows continue to be added each day until the monthly aggregation summarizes the data and archives the original transactions to tape. Each transaction row is 100 bytes, meaning that 150 MB of storage is used each day, in addition to the 50 MB necessary for indexes. Therefore, Guttbaum’s data structure, which requires 200 MB per day of disk storage, requires a monthly total of disk space equaling 200 MB X 30 days, or about 6 GB. Figure 3.6 displays Guttbaum’s existing OLTP environment.


Figure 3.6 An overview of Guttbaum’s existing OLTP environment.

Until recently, Guttbaum’s was only able to track purchases at the point of sale with their scanners. This didn’t allow any understanding about the type of customer making the purchase. However, the introduction of the data warehouse provides an opportunity for this to change.

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This is an excerpt from "High Performance Data Warehousing", copyright 1997.

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