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Database Management System

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DBM405 (Database Management System)

Facilitator Anup Majumder-University of Phoenix Online


The research that I have done for this individual assignment where I need to provide a brief explanation of OLAP, Data Warehouse and Data Mart, Three-tier architecture and ASP was through a research online. I found that most of these acronyms for computer terminology are hard to find when the meanings that apply to your subject or acronym you are un-aware of it. In conclusion all the above words are related one to each other.

Let me start this paper providing a shot briefing about how hard was for me finding most of the explanation of these four terminologies that I will be explaining below.

OLAP is the first word that I will define in my paper. OLAP is an acronym for On-Line Analytical Processing. It is an approach to quickly providing the answer to complex database queries. Is used in today business for reporting sales, marketing, management reporting, data mining and similar areas.

The main reason of using OLAP to answer queries is speed. Relational databases store entities in discrete tables if they have been properly normalized. This structure is good for operational databases but for complex multi-table queries is comparatively slow. A better model for querying but worse for operational use is the dimensional database.

OLAP take a snapshot of a relational database and restructures it into dimensional data. The queries can ten be run against this. It has been claimed that for complex queries OLAP can produce a result in around 0.1% of the time for the same query on relational data.

For example a set of customers can be grouped by city, by district or by country; so with 50 cities, 8 districts and two countries there are three hierarchical levels with 60 members. These customers can be considered in relation to products; if there are 250 products with 20 categories, three families and three departments then there are 276 product members. With just these two dimensions there are 16,560 possible aggregations. As the data considered increases the number of aggregations can quickly total tens of millions or more.

The above example from give us a better picture how many more aggregations are add without notice it. Thanks to OnLine Analitical Processing minized the time to a result in around 0.1% of the time for the same query on a relational data.

The second terminology that I research was Data Warehouse and Data Mart. Data Warehouse and Data Mart is coorporate memory. Let's take this other terminology and break it in two part; one Data Warehouse and the second one Data Mart. Starting by Data Warehouse, academics will say it is a subject oriented, point-in-time, inqury only collection of operational data. And what this mean? Easy. Typical relational databses are designed for On-Line Transactional Processing (OLTP) and do not meet the requirements for effective On-Line Analytical Processing (OLAP). As a result data warehouses are designed differently that traditional relational databases.

Now that we just explained the first part of this second terminoly lets take the second part; Data Mart. Data Mart is a miniature data warehouse; in others words is just on segment of the organization. A good example is the one that Microsoft Developer Network provide us in his page: A large service organization may treat regional operating centers as individual business units, each with its own data



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