Business information system - Essay Example

Assignment Data Warehouse Bill Inmon is the first person who introduced the term Data Warehouse in 1991. Data Warehouse is large collection of an organizations past and present Business data. The basic aim of Data warehouses is to provide the facility of business reporting and analysis. A data ware house can have million of rows of data and its size can be in terabits. It is a relational database so it stores data in from of rows and tables. It uses standard query language. It becomes available 7*24.

It has data size varying from business to business. Like in telecom sector you have lot of data you can not retain it for years, but in insurance business you have keep data of many years (Inmon 1995). In this paper we will discuss different aspects of data warehouse.This section will explain four main characteristics of data warehouse. These characteristics give a more clear view of data warehouse. First characteristic of data warehouse is “Subject Oriented” (Kimball & Ralph 2002), It means data warehouse should be built for a specific subject or domain.

It also means that Data that provides information about the concerning topic/ subject instead of organizations ongoing processes. Second characteristic is “integrated” nature of data warehouse. It Means data that is gathered from different sources are integrated into a single structure. Third characteristic is “Time-variant”: It means all data that is stored in the data warehouse is recognized with a specific time period. Forth and last characteristic is “Non-volatile” nature of data warehouse.

Data is never deleted from data warehouse. But when it exceeds from a certain level, it is archived and stored at differ places (Kimball & Ralph 2002).There is a question about data warehouse: When data warehouse has data in terabits, then how it can handle such amount of data? The answer is: data warehouse uses parallelism approach to handle such amount of data. By this approach data is divided into small parts and parallel unit of data warehouse store retrieve and process them individually (Inmon 1995).

Architecture of data warehouse consists of different logical layers (Inmon 1995). First layer is input or source layer, then processing layer, out put layer, decision support layer, etc. At source layer we have our data sources; these can be data bases, legacy system or data from web. After extracting this data from the source a process of transformation is applied on it to bring it into coherent format. Because data can be extracted from any type of sources, it contains dirty data that must be purified.

After this step data is loaded into data warehouse. These are three steps in which data from sources to data warehouse transformed. These steps are called ETL (extraction, Transformation, Loading) (Inmon 1995). After data loading the data is stored in from rows and columns. Here data can be arranged in two sub parts those called “Data marts”. These can be referred as sub-section (parts) of data warehouse. Like if you have data warehouse of an organization, here you can divide your data warehouse in Accounts, Production, and Sales Data Marts.

This provides ease in data handling, querying, recovery and maintenance. After maintaining your data in data warehouse you apply decision support and decision making techniques on data warehouse, which include data mining, OLAP, querying tools and visualizing techniques. These techniques help in decision making and future perditions about business trends (Kimball & Ralph 2002).We have talked about the design of data warehouse; it has star schema approach for its design methodology. In this schema we have a large single fact table and lots of other tables are connected with it with a single link.

Here we renormalize data to increase the number of table. Increasing the number of tables improves retrieval time. So that data warehouse able to answer the quires with even large amount of data. Data warehouse life cycle is completely opposite to other software development life cycle (Inmon 1991).References 1. Inmon, (1995). Tech Topic: What is a Data Warehouse? Prism Solutions. Volume 1.2. Kimball, Ralph and Ross. (2002). Margy. The Data Warehouse Toolkit Second Edition John Wiley and Sons, Inc.

ISBN 0-471-20024-7