Data Warehouse Concepts - Data Warehouse Systems: Design and Implementation - page 52

fast, concurrent access to data. This requires transaction processing and
concurrency control capabilities, as well as recovery techniques that guarantee
data consistency. These systems are known as operational databases or
online transaction processing ( OLTP ) systems. The OLTP paradigm
is focused on transactions. In the Northwind database example, a simple
transaction could involve entering a new order, reserving the products
ordered, and, if the reorder point has been reached, issuing a purchase order
for the required products. Eventually, a user may want to know the status
of a given order. If a database is indexed following one of the techniques
described in the previous chapter, a typical OLTP query like the above would
require accessing only a few records of the database (and normally will return
a few tuples). Since OLTP systems must support heavy transaction loads,
their design should prevent update anomalies, and thus, OLTP databases
are highly normalized using the techniques studied in Chap. 2 .Thus,they
perform poorly when executing complex queries that need to join many
relational tables together or to aggregate large volumes of data. Besides,
typical operational databases contain detailed data and do not include
historical data.
The above needs called for a new paradigm specifically oriented to
analyze the data in organizational databases to support decision making.
This paradigm is called online analytical processing ( OLAP ). This
paradigm is focused on queries, in particular, analytical queries. OLAP-
oriented databases should support a heavy query load. Typical OLAP queries
over the Northwind database would ask, for example, for the total sales
amount by product and by customer or for the most ordered products by
customer. These kinds of queries involve aggregation, and thus, processing
them will require, most of the time, traversing all the records in a database
table. Indexing techniques aimed at OLTP are not ecient in this case: new
indexing and query optimization techniques are required for OLAP. It is easy
to see that normalization is not good for these queries, since it partitions the
database into many tables. Reconstructing the data would require a high
number of joins.
Therefore, the need for a different database model to support OLAP
was clear and led to the notion of data warehouses , which are (usually)
large repositories that consolidate data from different sources (internal and
external to the organization), are updated off-line (although as we will see,
this is not always the case in modern data warehouse systems), and follow
the multidimensional data model. Being dedicated analysis databases, data
warehouses can be designed and optimized to eciently support OLAP
queries. In addition, data warehouses are also used to support other kinds of
analysis tasks, like reporting, data mining, and statistical analysis.
Data warehouses and OLAP systems are based on the multidimensional
model ,whichviewsdatainan n -dimensional space, usually called a data
cube or a hypercube . A data cube is defined by dimensions and facts.
Dimensions are perspectives used to analyze the data. For example, consider