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Data Transformation Analysis

Oracle Tips  by Burleson Consulting

The Data Warehouse Development Life Cycle


In the next chapter, we’ll also discuss data warehouse design where physical techniques are used to store unique data attributes within data warehouse tables. For example, some grocery attributes are specific to a class of items, such as the shelf life data attribute that applies to calf livers, but not to paper towels. These are just some of the types of data issues that must be addressed in a data warehouse design.

This leads us to the second task in data transformation analysis--data dissection. Let’s start with an example. Your end users state that they want to analyze sales by date. Armed with this knowledge, you note that a date column is necessary, and you don’t give the issue another thought. However, what if your end users want to compare sales for the same months, except in different years? What if an end user wants to compare sales by quarter? In order to quickly service these queries, you’ll want to decompose the date component into its component parts. With some thought, you may be able to identify many other data items that need to be decomposed for representation in your data warehouse.

Data dissection implies that the data has a functional dependency upon the derived components. As you may recall from basic data normalization theory, full functional dependence is required for data models to participate in second normal form (2NF) and third-normal form (3NF) relations. Functional dependence implies that one “key” value will be the determinant for another “non-key” value. For example, if we know the date, we also know the day, month, quarter, and year. If we know a ZIP code, we therefore could know the state, city, and metropolitan area.

While this analysis may seem obvious, the decomposition of these types of column values can greatly aid the query speed for Oracle databases. Let’s consider a simple example using a date column. As you may know, a date column datatype contains the century, year, day, month, and time. However, getting these values out of a date datatype is not always easy, especially when groupings such as the quarter of a year are desired. This type of date transformation also has a nasty side effect of invalidating the use of date indexes in Oracle, causing painful full-table scans. However, if we break out the date datatype into its component year, month, and day values, we see that SQL queries can be greatly simplified, as shown in Table 3.3.

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