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The Data Warehouse Development Life Cycle


Type      With date Datatype                           With date Breakout
Year      to_char(sales_date,’YYYY’) = ‘1997’            sales_year = 1997
Month     to_char(sales_date,’MM’) = ‘MAR’              sales_month = ‘MAR’
Quarter   sales_date between ‘01-JAN-97’ and ‘31-MAR-97’ quarter = ‘SPRING’

Table 3.3 date datatype predicates.

This get even more obvious when we try to compare sales for the same month in two different years. Listing 3.4 compares sales using SQL code implementing the date datatype, and Listing 3.5 compares sales using SQL code that breaks date out into three columns.

Listing 3.4 SQL using the date datatype.
select sum(sales)
to_char(sales_date,’MM-YY’) = ‘MAR-96’
group by to_char(sales_date,’MM-YY’) = ‘MAR-96’;

Listing 3.5 SQL breaking date out into three columns.
select sum(sales)

Here, you can clearly see that avoiding the use of the to_date and to_char built-in functions within Oracle can greatly simplify complex data warehouse SQL queries. For details on breakouts of datatypes for data warehouses see Chapter 13, Tuning Oracle SQL.

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