Answer Posted / kashish
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.
Advantages:
• Excellent performance: MOLAP cubes are built for
fast data retrieval, and is 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.
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.
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.
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