Database Modeling and Design

166 CHAPTER 8 Business Intelligence
Invoice Date
Job Costing Daily Snapshot
«fk» invoice date id
widget quantity
estimated hours
estimated cost
price
actual hours
actual cost
Figure 8.12
Schema for the job costing daily snapshot
The schema for the job costing daily snapshot is shown in Figure 8.12.
Notice that most of the dimensions used in the job-costing detail are not
used in the snapshot. Summarizing the data has eliminated the need for
most dimensions in this context. The daily snapshot contains one row
for each day that jobs have been invoiced. The number of rows in the
snapshot would be in the thousands. The small size of the snapshot
allows very quick response when a user requests the job costing daily
snapshot. When there are a small number of summary queries that
occur frequently, it is a good strategy to materialize the summary data
needed to answer the queries quickly.
The daily snapshot schema in Figure 8.12 also allows the user to
group by month, quarter, or year. Materializing summary data is useful
for quick response to any query that can be answered by aggregating the
data further.
8.2 Online Analytical Processing (OLAP)
Designing and implementing strategic summary tables is a good
approach when there is a small set of frequent queries for summary data.
However, there may be a need for some users to explore the data in an ad
hoc fashion. For example, a user who is looking for types of jobs that
have not been profitable needs to be able to roll up and drill down various
dimensions of the data. The ad hoc nature of the process makes predicting
the queries impossible. Designing a strategic set of summary
tables to answer these ad hoc explorations of the data is a daunting task.
OLAP provides an alternative. OLAP is a service that overlays the data
warehouse. The OLAP system automatically selects a strategic set of summary
views, and saves the automatic summary tables (AST) to disk as
materialized views. The OLAP system also maintains these views, keep-