The Data Warehouse Development Life Cycle
Data Warehouse Analysis
Another major difference between analysis for OLTP systems and data
warehouse systems lies in the descriptions of the user interface. In
a traditional analysis, careful attention is given to the method the
end user will implement when interacting with the system. In a data
warehouse analysis, developers expect that most, if not all, queries
against the warehouse will be ad hoc. As such, data warehouse
developers have more interest in specifying the data for the
warehouse than for specifying the data access methods.
Do not confuse the analysis of data access methods with the analysis
of data query requirements. While you may not care what tool is used
to extract queries from your warehouse, you should care very much
about what types of queries will be executed against your warehouse.
We will discuss this issue later in this chapter.
Another analysis issue comes into play because Oracle warehouse
developers know in advance that they are using an Oracle database to
implement the warehouse. Oracle, by virtue of being relational, is
inherently flexible, and it is relatively easy to drop or add data
columns to table structures. A consequence of this reality is that
developers can often begin prototyping the data warehouse before a
complete analysis of data sources has been completed. To demonstrate
this flexibility, consider the following code where we quickly alter
our fact table to drop one column and add another:
SQL > alter table fact drop column customer_type;
column dropped
SQL > alter table fact add column total_sale int;
column added.
Of course, the flexibility of the
relational architecture is only useful when prototyping the
warehouse. The final data items should be carefully considered
before the initial rollout of the system because altering a very
large table can cause a huge amount of table fragmentation in a
production warehouse. To fully appreciate the differences between
traditional systems analysis and data warehouse analysis, let’s
review the analysis steps for both traditional data processing
systems and data warehouses.
This is an excerpt from "High Performance
Data Warehousing".
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