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Oracle Tips 

by Burleson Consulting

The Data Warehouse Development Life Cycle

LEGACY DATA ANALYSIS

Another source of external data consists of legacy data systems. It is not uncommon for analysts to discover that one class of data resides in different database formats. For example, sales data from 1980 through 1990 may reside on an IMS database, while transactions from 1991 to present are stored in a DB2 database. The data analyst must be able to deal with the problem of translating data archive tapes in varying formats.

In some cases, developers find it fortunate that a data source is stored in an OLTP Oracle database and believe that the data extraction and loading will be easier because the source and target data sources reside within Oracle. However, it must be noted that many of the standard Oracle utilities for data extraction and loading (export-import) are of no use with Oracle data warehouse loading. This is because of the data transformation that takes place as a part of data extraction and loading. For example, the de-normalized format of the data warehouse makes it impossible to export an OLTP Oracle database that uses five tables to represent a sales transaction.

As a general rule, data extraction involves formatting legacy data for loading into the warehouse. For example, data could be extracted directly from an Oracle OLTP system into the de-normalized Oracle warehouse as shown in Listing 3.3.

Listing 3.3 One-step data extraction and load between Oracle databases.

Create table FACT
as
select customer_name,
customer_address,
sales_date,
to_char(sales_date,’YYYY’),
to_char(sales_date,’MM’),
to_char(sales_date,’DD’),
sale_amount,
quantity_sold,
total_price,
from

customer@oltp cust,
sales@oltp sale,
line_item@oltp li
item@oltp item

SEE CODE DEPOT FOR FULL SCRIPT

Here, you can see that the data is pulled from the Oracle OLTP database using Oracle’s SQL*Net facility. For details on this distributed SQL technique, see Chapter 9, Distributed Oracle Data Warehouses. Also, note the dissection of the sales_date column. Rather than store the sales_date as a single column of the date datatype, the Oracle data warehouse transformation stores the sales_date in three columns: one for the day, another for the month, and a third column for the year. The reason for this date breakout will become clear later in this chapter when we discuss data query analysis.


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:
http://www.rampant-books.com/book_1002_oracle_tuning_definitive_reference_2nd_ed.htm
 

 


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