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Oracle Tips
by Burleson Consulting
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The Data Warehouse Development Life Cycle
The Role Of Functional Decomposition
The principles of top-down analysis tells us to begin our data flow
diagram (DFD) at a very general level. The entire system is viewed
as a single process, and this view is called a Context Level DFD.
Next, the DFD is decomposed, and levels of detail are added to the
model. Any process that can be identified can probably be subdivided
to smaller processes, and it is possible to decompose a DFD to the
level where each process represents a single statement. An extreme
example of functional decomposition would be showing a statement
such as add 1 to counter as a separate process on the data flow
diagram. The pivotal question is: At what point should the developer
stop decomposing the processes?
Theoreticians such as Gane and Sarson tell us that a DFD should be
decomposed to the functional primitive level, where each process
bubble performs one granular function. Under this definition, one
could consider that the place_an_order behavior performs only one
function and is therefore a functional primitive process. A good
rule of thumb for data warehouse analysis, especially when the
warehouse is intended to be used with a relational database, is that
a DFD should be decomposed to the level where each process
corresponds to an SQL operation. This allows the use of triggers
within a relational database and greatly simplifies the data
warehouse design.
As you are probably beginning to see, the level of partitioning is
critical for a successful data warehouse systems analysis. Let’s
explore this concept of partitioning a little further. In a
traditional systems analysis, the place_order behavior is
sufficiently partitioned, whereby the place_order process would
become a single computer program. This program would perform all of
the data manipulation and would have many DML verbs embedded within
the code (see Figure 3.4). Following is what the mini-spec might
look like for a place_order process:
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