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Field names are shown with syntax of tablename.columnname. The Count and Sum keyword
perform aggregation based on the items listed in the GROUP BY clause. The AS keyword indicates
that the column should be titled something other than its default caption. The FROM keyword is used
to indicate the tables to be used. The INNER JOIN clause indicates how the data in the tables should
be integrated (linked). The HAVING clause shows the restriction—in this case only customers whose
count of bikes sold is >1. The ORDER BY clause is the sort order.
Data Warehouse
Data warehousing attempts to reconcile and integrate data from legacy applications software with
today‟s newer technology. As mentioned earlier, industry is rife with older legacy systems that are
currently cost prohibitive to replace. Most of these older systems are mission-critical operational
transaction systems and satisfy most of the operational needs of the company. However, they are built
on technology that cannot support the kinds of decision support tools that management requires. Many
of these systems use older file structures or obsolete database management systems and are almost
incapable of accessing and manipulating data.
As an alternative to replacing these systems, data warehousing provides a state-of-the-art database
management system that is fed data from the older legacy systems. However, data does get duplicated,
which can potentially cause a synchronization problem between the data in the warehouse and the data
in the older legacy systems. Consequently, IT management must put stringent controls in place. Still,
the benefits outweigh the potential problems, for the data warehouse comes with all of the high-tech
tools that will enable management to create a plethora of queries and reports. Most of the newer
business intelligence tools mentioned earlier require a storage capability similar to the data warehouse.
Any organization that implements a data warehouse must create a corporate data directory for the
data that will reside in the data warehouse. This metadata includes attributes (name, size, data type,
etc.); data about the data (where it is located, which application owns it, what data it is associated
with); and descriptive information (context, quality, condition). Software tools follow these rules to
extract, transform, and load operational data into the data warehouse. For example, the metadata will
tell an end user that the data field called “sales” means “net sales” and comes from the general ledger
application as the aggregate of all delivered orders for the period. Ideally, data elements should have
names that are agreed to by those who work with the data—so maybe, in this example, the field would
be called “net sales” to begin with.
Exhibit 20.8 Snowflake schema.