Categories
Of OLAP Tools
MOLAP
This is
the more traditional way of OLAP analysis. In MOLAP, data is stored in a
multidimensional cube. The storage is not in the relational database, but in
proprietary formats. That is, data stored in array-based structures.
Advantages:
·
Excellent performance: MOLAP cubes are built for
fast data retrieval, and are optimal for slicing and dicing operations.
·
Can perform complex calculations: All calculations
have been pre-generated when the cube is created. Hence, complex calculations
are not only doable, but they return quickly.
Disadvantages:
·
Limited in the amount of data it can handle:
Because all calculations are performed when the cube is built, it is not
possible to include a large amount of data in the cube itself. This is not to
say that the data in the cube cannot be derived from a large amount of data.
Indeed, this is possible. But in this case, only summary-level information will
be included in the cube itself.
·
Requires additional investment: Cube technology are
often proprietary and do not already exist in the organization. Therefore, to
adopt MOLAP technology, chances are additional investments in human and capital
resources are needed.
Figure
Describes The Relation of the MOLAP with the server and end user.
Examples:
Hyperion Essbase, Fusion (Information Builders)
ROLAP
This
methodology relies on manipulating the data stored in the relational database
to give the appearance of traditional OLAP’s slicing and dicing functionality.
In essence, each action of slicing and dicing is equivalent to adding a ―WHERE‖
clause in the SQL statement. Data stored in relational tables
Advantages:
·
Can handle large amounts of data: The data size
limitation of ROLAP technology is the limitation on data size of the underlying
relational database. In other words, ROLAP itself places no limitation on data
amount.
·
Can leverage functionalities inherent in the
relational database: Often, relational database already comes with a host of
functionalities. ROLAP technologies, since they sit on top of the relational
database, can therefore leverage these functionalities.
Disadvantages:
·
Performance can be slow: Because each ROLAP report
is essentially a SQL query (or multiple SQL queries) in the relational
database, the query time can be long if the underlying data size is large.
·
Limited by SQL functionalities: Because ROLAP
technology mainly relies on generating SQL statements to query the relational
database, and SQL statements do not fit all needs (for example, it is difficult
to perform complex calculations using SQL), ROLAP technologies are therefore
traditionally limited by what SQL can do. ROLAP vendors have mitigated this
risk by building into the tool out-of-the-box complex functions as well as the
ability to allow users to define their own functions.
Figure
describes The Relation of the ROLAP with the server and end user.
Examples:
Microstrategy Intelligence Server, MetaCube (Informix/IBM)
HOLAP (MQE: Managed Query Environment)
HOLAP
technologies attempt to combine the advantages of MOLAP and ROLAP. For
summary-type information, HOLAP leverages cube technology for faster
performance. It stores only the indexes and aggregations in the
multidimensional form while the rest of the data is stored in the relational
database.
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