OLAP, Data Marts and Warehouses, Three-Tier Archit Essay

This essay has a total of 1873 words and 9 pages.

OLAP, Data Marts and Warehouses, Three-Tier Architecture and ASP



OLAP, Data Marts and Warehouses, Three-Tier Architecture and ASP
The term OLAP stands for ‘On-Line Analytical Processing'. OLAP is a technology used to
process data a high performance level for analysis and shared in a multidimensional cube
of information. The key thing that all OLAP products have in common is
multidimensionality, but that is not the only requirement for an OLAP product.

An OLAP application is targeted to deliver most responses to users within about five
seconds, with the simplest analyses taking no more than one second and very few taking
more than 20 seconds. Impatient users often assume that a process has failed if results
are not received with 30 seconds, and they are apt to implement the ‘3 finger salute' or
‘Alt Ctrl Delete' unless the system warns them that the report will take longer. Even if
they have been warned that it will take significantly longer, users are likely to get
distracted and lose their chain of thought, so the quality of analysis suffers. This speed
is not easy to achieve with large amounts of data, particularly if on-the-fly and ad hoc
calculations are required. A wide variety of techniques are used to achieve this goal,
including specialized forms of data storage, extensive pre-calculations and specific
hardware requirements, but a lot of products are yet fully optimized, so we expect this to
be an area of developing technology. In particular, the SAP Business Warehouse is a full
pre-calculation approach that fails as the databases simply get too. Likewise, doing
everything on-the-fly is much too slow with large databases, even if the most expensive
server is used. Slow query response is consistently the most often-cited technical problem
with OLAP products.

OLAP is used for mainly for analysis. This means that the system copes with any business
logic and statistical analysis that is relevant for the application and the user, and keep
it easy enough for the target user. This analysis is done in the application's own engine
or in a linked external product such as a spreadsheet. All the required analysis
functionality can be provided in an intuitive manner for the target users. This could
include specific features like time series analysis, cost allocations, currency
translation, goal seeking, ad hoc multidimensional structural changes, non-procedural
modeling, exception alerting, data mining and other application dependent features.

The OLAP system implements all the security requirements for confidentiality. Not all
applications need users to write data back, but for the growing number that does, an OLAP
system handles multiple updates in a secure manner.

Multidimensional data is a key requirement. If one had to pick a one-word definition of
OLAP, this is it. The OLAP system provides a multidimensional conceptual view of the data,
including full support for hierarchies and multiple hierarchies, certainly the most
logical way to analyze your business or organization.

Information is gathered based on business needs, wherever it is and however much is
relevant for the application. The sure capacity of various applications in terms of how
much inputted data, differs greatly — the largest OLAP applications can hold at least a
thousand times as much data as the smallest. Many considerations are made here, including
data duplication, memory requirements, disk space utilization, performance, integration
with data warehouses and the like.

Most data in OLAP applications originates in other systems. However, in some applications
(such as planning and budgeting), the data might be captured directly by the OLAP
application. When the data comes from other applications, it is usually necessary for the
active data to be stored in a separate, duplicated, form for the OLAP application. This
may be referred to as a data warehouse or, more commonly today, as a data mart.

The most common uses for a data warehouse include performance, multi-data stores, data
cleansing, data adjusting, timing, and historical analysis.

Data warehouses are often large, but are nevertheless used for unpredictable interactive
analysis. This requires that the data be accessed very rapidly, which usually dictates
that it be kept in a separate, optimized structure which can be accessed without damaging
the response from the operational systems.
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