Data Warehouse for a University Literature Review

1Data Warehouse for a University Literature Review
DATA WAREHOUSE FOR A UNIVERSITY LITERATURE REVIEW
[Student name]
[University name]
[Professor Name]
[Date]

2Data Warehouse for a University Literature Review
Table of Contents
1 Definitions of data warehouse.............................................................................. 3
2 Characteristics of data warehouse..................................................................................3
3 Life cycle of the data warehouse..........................................................................5
4 Architectures and approaches to the data warehouse.........................................9
4.1 Top-down(Bill Inmon) Data Warehouse Design Approach............................10
4.2 Bottom-up(Ralph Kimball) Data Warehouse Design Approach.....................11
5 Advantages and Disadvantages of the data warehouse.......................................................14
6 General data warehouse implementation papers..............................................15
7 Data warehouse implementation in universities................................................16
7.1 Logical Design of Data Models.....................................................................17
7.2 Physical Design of Data Models............................................................................18
7.3 Application Server implementation........................................................................18
References............................................................................................................... 19

3Data Warehouse for a University Literature Review
1 Definitions of data warehouse
The data warehouse is a type of technique that is used to do data collection and management
from various sources in order to provide very meaningful businesses insight. The data warehouse
is also termed as the technology and the components that help in use of the data in a strategic
manner.
However it is also an electronic storage of the large amount of data and information by the
businesses and organizations owners that is used for querying and analyzing instead of using the
transactions processes.
The data warehouse also is type of processes used to do transformation of data into meaningful
information and also makes it available to the various users on time in order to make difference.
2 Characteristics of data warehouse
The data warehouse being a large storage tool to store the data that can be queried and analyzed
by the business owner or organizations in order to get the business insight has various properties
and characters which includes the following:
1. Subject-oriented.
The data warehouse is subject oriented since is used for delivering the information of the themes
of the organization instead of its currently running operations, and it can be achieved through
specific organization theme. Therefore the data warehouse processes is meant to handle a certain
or specific theme that well defined, among the defined themes can be sales, distributions, or even
marketing. However in data warehouse the current operation of the organization are not highly
emphasized on but the major focus is on demonstration and analysis of the data in order to make
various decisions. The data warehouse is also used to deliver easy and precise demonstrations
which revolved around a certain theme through the elimination of data that is not needed in the
decisions making.
2. Integrated

4Data Warehouse for a University Literature Review
The data warehouse is integrated in nature where it is found that shared entities to scale that is
similar from variety of data. In this case the data resides to various data warehouses in a shared
and general granted form.
The data warehouse however is built through the integration of data from many sources by which
can be mainframe and relational databases.
The data warehouse make use of the naming conventions, formats and coding that are fully
reliable In addition, it must have reliable and thus the integration of the data warehouse as a
result helps in the effectiveness of the data analysis process.
Therefore the naming conventions, column scaling, encoding structures must be confirmed to be
fully reliable and therefore the data warehouse integration is able to handle various subjects that
are related to warehouses.
3. Time-Variant.
In the data warehouses the data storage and maintenance is done in intervals like the in weeks,
months and yearly. There are various time limits that are structured between the various large
data sets and are maintained in the online transaction process (OLTP).
This time limits in the data warehouse has a wide range compared to the operational systems,
however any data that resides in the data warehouse can be predicted with the specified time
intervals that is used to deliver information from the various historical perspectives.
Therefore data warehouse comprise of the time explicit and implicit elements, and also other
time variance where the data stored in the data warehouse it can’t be changed or updated later.
4. Non-Volatile.
The data ware house is non volatile in that the stored data is permanent and that data already
stored cannot be either erased or deleted when a new set of record is inserted, this data consists
of the mammoth quantity of data which is inserted in the modifications between the selected

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quantities of the logic businesses and thus it is used in the analysis of the data within the
technology of the data warehouse.
In data warehouse the data is mainly on a read only formats that get refreshed at certain interval
and this benefits the analysis of historical data and also in comprehensions of the functionalities.
The data ware house do not relay on the transactions processes, recaptures and concurrency
controlling mechanisms, however there are various operations like deleting, updating and
inserting which are executed in the operational applications and are not applicable in the data
warehouse environments.
Therefore the only two operations done in the data warehouse data includes:
 Data Loading
 Data Access
5. It has some de-normalized data that enable simplifications and to improve the performances.
6. It makes use of large amount of historical data.
7. It uses queries to retrieve large amount of data.
8. It makes use of the ad hoc and planned type of data queries.
9. It has controlled data loading.
3 Life cycle of the data warehouse
The data warehouse life cycle is the phases that the data warehouse goes through form the time it
is conceived until when it is not available to be used.
The life cycle in the data warehouse various phases like the requirement analysis, data
warehouse designing /modeling, data warehouse constructions, data warehouse testing, data
warehouse deployment, data warehouse operations, data warehouse maintenance, and data
warehouse retirement as indicated in the figure below.

6Data Warehouse for a University Literature Review
Below are the various data warehouse life cycles phases descriptions.
 Requirements gathering
 Requirements analysis
 High level design (HLD)
 Low level design (LLD)
 Development – Automating scripts
 Testing
 User Acceptance Testing (UAT)
 Project release or deployment
 Warranty support
3.1.1.1 Requirements Gathering
This is the initial and very critical phase in the implementation of the data warehouse where the
completed and clearly defined requirements are defined for the proposed data warehouse
projects.
The gathering of the requirements can be done through the one on one meeting with the end
users or the clients of the projects where the business analysts and the development team
members are involved.
And therefore a Business requirement document (BRD) is prepared and the following are the
steps of the requirements gathering.
i. Understanding the current data models and preparation of the questionnaires for the new
data warehouses user.