Warehousing and Business Intelligence

Running head: DATABASE CONCEPTS - MODELLING AND SQL
DATABASE CONCEPTS - MODELLING AND SQL
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DATABASE CONCEPTS - MODELLING AND SQL
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Table of Contents
Section A: APPLIED DATABASE THEORY...............................................................................2
Warehousing and Business Intelligence......................................................................................2
Process and benefits.....................................................................................................................2
Section B: SQL................................................................................................................................3
Section C: DATA MODELLING....................................................................................................8
Question 1....................................................................................................................................8
Question 2....................................................................................................................................9
Question 3..................................................................................................................................10
Question 4..................................................................................................................................11
Referencing....................................................................................................................................12

DATABASE CONCEPTS - MODELLING AND SQL
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Section A: APPLIED DATABASE THEORY
Warehousing and Business Intelligence
Data Warehousing and Business intelligence are the part of BI (Business Intelligence)
Architecture. It describes the standards policies for data organization by the computer based
technologies to improve the business functionalities. The important and critical decisions are
taken with the help of data visualization, reports and then analysis to direct the business towards
organizational goals. Data warehousing is the component of this architecture, which is used in
performing the data cleaning, organization, filtering, storing and extracting in a centralized
manner (Thamir & Poulis, 2015). The important factors that contributes in the modern business
intelligence are data collection, integration, storage, analysis, distribution, and decisions on the
basis of the insights.
Process and benefits
Data warehousing is a method of data management in a centralized manner and to store
data from different sources into one storage. The main problem that warehousing solves is all the
data which always needed to be analyzed presents in a central storage system. It mainly has three
process that runs analysis of data which is ETL (Extract Transform and Load). ETL simplifies
the larger and complex data very effectively and efficiently (Anand, 2014). The warehousing has
become a very common in business organizations today. Some of the benefits of the Data
Warehouse for a business are discussed below:
i. Improved Business Intelligence: The analysis report from the data warehousing helps a
lot in improving the information or the access to it. Without having, complete
information a decision cannot be made in any business organization. These decisions are

DATABASE CONCEPTS - MODELLING AND SQL
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mainly based on the data driven facts and supported by the queries and information
related to the company.
ii. Improved performance: Data warehouses are designed to achieve a faster retrieval of
the data using any query. The process of storing data is also fast in the database and it has
a larger capacity. It is designed different than the normal conventional databases to
achieve data cleansing, filtering, and analysis. Usually the Data warehouse follows the
Star or Snowflake schema for the implementation (Warnars & Randriatoamanana, 2016).
iii. Easily Accessible: In data warehouses, the users and the companies can have easy access
and on the data for storage and retrieval both. The runtime for ETL in a data warehouse
is small than the other conventional databases as it follows the specific type of schemas
which has less chains of relations between the entities. The interface implementations
can also be done with the data warehouses.
iv. Quality and Consistency: The data is stored in a data warehouse comes from multiple
different sources which cannot be stored directly into the storages. The data is first
cleaned, filtered, converted and processed, then only it is stored in the system (Ilyas &
Chu, 2015). This consistency and standardization of the data allows the users to use the
same data for analysis, query and other operations.
v. High value return: The data warehouse helps in generating an improved profits from the
cost and time saving operations. The data ware housing has become a standard
methodology for increasing business intelligence which justifies the successful impact of
data warehousing for any business.
Section B: SQL
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