Database Management System Overview

This report provides a detailed overview of Database Management Systems (DBMS), focusing on MySQL, its features, and applications. It discusses data modeling, the importance of business rules in database design, and the challenges posed by Big Data. The report also highlights current technologies that address these challenges, making it a comprehensive resource for understanding DBMS.

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DATABASE MANAGEMENT SYSTEM
By Student’s Name
Course
Lecturer
University

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Question 1
(a) Define the following:
i. Data is raw information such as symbols or alphabets.
ii. Information is accurate and timely data that is organized for a particular purpose and is
presented in a manner that has a meaning and enhance understanding.
iii. A database is a collection of information organized for fast search and retrieval by computer.
iv. A database management system (DBMS) is a software application designed to interact with
database, users, and other applications to obtain and analyze data (Kedar, 2009).
v. A database system environment is a system of components including users, data, software,
database handling techniques, and hardware which regulate group of data and data use and
management.
(b)
Storing and retrieving data and information involves a database which is accessed via a database
management system. With the DBMS, data can be inserted into the database and organized in a
presentable way. The DBMS also is used to retrieve information stored. The process of data
storage and retrieval relies on the database system environment whose components facilitate data
entry and access.
(c) Applications supported by database technologies.
Some applications supported by database technologies include:

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Social media sites - Sites such as Facebook and LinkedIn relies on database technologies to store
user details.
E-commerce sites – Sites such as Amazon and Walmart use database technologies to store
product catalog and user details.
Online Library – An online library is powered by database technologies which allow storage and
retrieval of digital copies of books.
Question 2
(a) Describe DBMS (MySQL):
i. Technical details
MySQL runs on all operating systems including LINUX and Windows. It can be used in many
applications and is popularly used by web-based applications. It’s a light-weight application that
is included as a component of LAMP, WAMP, and XAMPP – enterprise stack used for web
development.
ii. Features
MySQL has a cross-platform support and supports a wide subset of ANSI SQL 99. It supports
stored procedures, triggers, online data definition language, updatable views, information
schema, and cursors (MySQL, 2007). The DBMS has a performance schema for monitoring
proposes and a several SQL model options that control runtime behavior. MySQL supports
distributed transaction support and ACID compliance when InnoDB is used. Other features
supported by the DBMS include full-text indexing and searching, Sub-SELECTs, query caching,

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SSL support, Built-in replication support, partitioned tables, commit grouping, and native storage
engines.
iii. Running environment.
MySQL can run in different operating systems including MAC, Windows, Linux, and UNIX. It
supports multiple users as various users can access the database while maintaining ACID
compliance. MySQL is a key component of open server web development stacks such as LAMP
and XAMPP.
(b) MySQL commands for in storing, retrieving, updating, and deleting data.
Store Retrieve Update Delete
Data INSERT INTO
table1 (name,
age) VALUES
(‘Myname’, 12)
SELECT *
FROM table1
UPDATE table1
SET
name=’James’
WHERE ID=1
DELETE FROM
table1 WHERE
ID=1
Question 3
(a) Define the following:
i. Data modeling is the process of representing a system design as a diagram that includes texts
and symbols indicating how data flows.
ii. A data model is a fundamental entity that includes attributes, entity types, relationships,
integrity rules, and define how data is processed.
(b) Importance of concepts from 3(a) to database design

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For a database design to be developed, data modelling has to be involved as it represents data
flow in a system which assists in mapping key entities, attributes, and relationships between the
entities that are incorporated in a data model.
(c) Define the following:
i. Entity-Relationship (E-R) model is a way of representing relationships of objects (Thalheim,
2013).
ii. Relational database model is a data management approach that involves a structure and
language representing data in terms of tuples grouped into relations (Paredaens, 2012).
(d) Relationship between these two models from 3(c)
E-R model is a graphical representation of the relational database model while the latter is an
implementation of the former. Converting E-R model to relational database model involves
creating tables and columns as graphically depicted. To convert relational database model to E-R
model, tables, columns, and relationships have to be graphically be represented as entities,
attributes, and relationships respectively.
Question 4
Gathering business rules is the first step of database design that involves defining rules based on
data is perceived and used which is dependent on the functions of an organization (Maciol,
2008).
E-R modeling involve graphically representing the entities and attributes identified after
developing the business rules.
Relational modeling involves implementing E-R model developed in the previous step.

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Normalization is the last step which involves organizing the tables and columns created to reduce
data redundancy.
Question 5
(a) Big data is a large data set that firms mine and analyze to gain insight into patterns that can
lead to business gains.
(b) Challenges the database technology is facing regarding Big Data
The volume of Big data creates challenges related to storing and processing it. Trends such as
social media, ecommerce sites, and Internet of things generate a lot of information that may be
used by any organization. Due to the size of the data generated, organizations may face data
storage problems especially if they lack the hardware equipment and software required to obtain
and process the data (Tole, 2013).
Various data sources associated with big data creates data integration problems. Since big data
comes from different sources such as social media streams, documents, etc., combing this data
can be extremely difficult. Additionally, since organizations get similar data from various
systems and the data may differ, the data obtained have to be validated. However, the process of
validating each record obtained is difficult as organizations need robust systems that ensure the
accuracy of the process (Nasser, 2015).
(c) Current technologies that deal with Big Data.
Some of the technologies available that deal Big Data include:
 Search and knowledge discovery tools support extraction of insights from data residing in
different platforms and systems (Freitas, 2013).

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 Predictive analytics allow firms to discover and use predictive models by analyzing data
sources to mitigate risk.
 Stream analytics applications filter, analyze, and organize data from various live data
sources in different formats.
 Data virtualization delivers information from different sources in real-time.

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References
Freitas, A. A. (2013). Data mining and knowledge discovery with evolutionary algorithms.
Springer Science & Business Media.
Kedar, S., 2009. Database management system. Technical Publications.
Macioł, A. (2008). An application of rule-based tool in attributive logic for business rules
modeling. Expert Systems with Applications, 34(3), 1825-1836.
MySQL, A. B., & MySQL, A. B. (2007). The world’s most popular open source
database. MySQL AB.
Nasser, T., & Tariq, R. S. (2015). Big data challenges. J Comput Eng Inf Technol 4: 3. doi:
http://dx. doi. org/10.4172/2324, 9307(2).
Paredaens, J., De Bra, P., Gyssens, M., & Van Gucht, D. (2012). The structure of the relational
database model (Vol. 17). Springer Science & Business Media.
Thalheim, B. (2013). Entity-relationship modeling: foundations of database technology. Springer
Science & Business Media.
Tole, A. A. (2013). Big data challenges. Database Systems Journal, 4(3), 31-40.