For those users,
hiding many of the individual attributes keeps them from feeling overwhelmed when
they see a huge list of possible values from which to choose.
Summary
Building a data warehouse is not necessarily a simple procedure. After choosing a
business problem to address and solve, the next step is to identify the sources for
the data and construct a relational data warehouse to hold a consolidated, consistent
version of that data. Next, an ETL process is created to move and transform the data
from its source systems into the star schema. Remember that the ETL process is
often the bulk of the effort, sometimes accounting for up to 80 percent of the time
on the overall project. Realize too that while the entire project will likely require
ongoing maintenance, it is the ETL that is often visited repeatedly as new cases of
bad data are identified and addressed.
After the ETL portion is completed, the cube itself is designed. If the design
on the star schema was good, the cube design is nearly done, at least from a high
level. The DSV provided by Analysis Services is a powerful tool that allows even a
well-designed star schema to be extended by renaming tables and columns, adding
calculated columns, adding named queries to act as view, and so forth. The DSV step
is necessary as cubes can only see DSVs as their data source.
60 B u s i n e s s I n t e l l i g e n c e w i t h M i c r o s o f t O f f i c e P e r f o r m a n c e P o i n t S e r v e r 2 0 0 7
Once the DSV is ready, the cube is built.
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