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