The Data Warehouse Development Life Cycle
Online Analytical Processing and Oracle
Relational OLAP (ROLAP)
The advent of the multidimensional
database led to an effort by tools vendors to create a method where
data could be extracted from a relational database and presented to
end users as if it were from a multidimensional database (see Figure
5.17). There are several methods for accessing a relational database
and presenting aggregated data as if it were from a multidimensional
database. These alternatives include ROLAP middleware tools and
downloading pre-aggregated data to local pivot tables. Another
common approach is to insert a metadata server between the OLTP
relational database and the query tool.
Figure 5.17 Overview of a ROLAP system.
In order to be considered a ROLAP
product, a tool must extract runtime data from a relational
database, present summarized data in cross-tabular format, and
possess a mechanism for translating the relational design into a
multidimensional format. Examples of popular ROLAP products include:
* DSS Agent by Microstrategies
* Metacube by Stanford Technology
Group
* Holos by Holistic Systems
* AXSYS Suite by Information
Advantage
* Red Brick Warehouse by Red
Brick Systems
* Prodea Beacon by Platinum
Technology
ROLAP systems provide extremely
flexible query engines by making any number of operational data
stores available to end users. These back-end databases are usually
relational databases, but ROLAP tools can also extract from a
variety of different relational database.
ROLAP tools require the definition
of the mapping between the OLAP model and the relational database,
and generate SQL to extract the required data from the operational
databases in a very similar fashion to the SQL generators described
in Chapter 4.
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