This document provides an overview of database management systems (DBMS). It begins by defining data and information, and explaining how data is organized and stored in databases. It then discusses different database models including hierarchical, network, relational, object-oriented, and semi-structured models. Key concepts like data normalization, integrity constraints, and security protocols in DBMS are also summarized. Examples of database usage for applications like online directories, billing systems, and social networks are provided to illustrate real-world DBMS implementations.

1. INFORMATION TECHNOLOGY
FOR BUSINESS-I
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2. 3
UNIT-3
DATABASE MANAGEMENT SYSTEM
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3. What is Data?
● Data is an individual unit that contains raw facts which do not carry
any specific meaning.
● Data can come in the form of text, observations, figures, images,
numbers, graphs, or symbols. When data arranged in an organized
form, can be called information.
● Data is organized in the form of graphs, charts or tables. There exist
data scientist who does data mining and with the help of that data
analyse our world.
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Example:-
You must have watched the weather forecast reports on news channels. They list the
minimum temperature, the maximum temperature, rainfall predictions and measurements.
The tabular representation is indicated below.
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4. Classification of Data?
Data is classified into
● Qualitative: It describes the quality of something or someone. It
is descriptive information.
For example, the skin colour, eye colour, hair texture, etc. gives
us the qualitative information about a person.
● Quantitative: It provides numerical information.
Example, the height and weight of a person.
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What is Information?
Information is processed, organised and structured data. It
provides context for data and enables decision making.
For example, a single customer’s sale at a restaurant is data –
this becomes information when the business is able to identify
the most popular or least popular dish.
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5. Difference Between Data and Information
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Types of information
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1. Conceptual information: Information which is based on ideas, concepts, theories, hypothesis etc,
and might be used in future or not. It does not always means the actual meaning. Such information do
not have scientific foundation.
● e.g. Charles Darwin’s Theory of Evaluation.
2. Empirical Information: The word empirical information denotes information acquired by means
of observation or experimentation. This information have scientific foundation.
● e.g. H2+O2=H2O (Water)
3. Procedural Information: The methodology which enables the investigators to operate more
effectively. Procedural information relates to means by which the data of investigation are obtained,
manipulated, and tested.
● e.g. police officer > Inform to public > Reason of investigation > To find out actual criminal.

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5. Policy Information: This type of information focuses on the decision making
process. It can be obtained from description, picture, diagram etc.
● e.g. Law and Justice.
6. Descriptive information: Information which deals with providing direction is
called directive information.
● e.g. Mode of operation in any organization.
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DATABASE

8. Database
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● A database is a systematic collection of data.
● They support electronic storage and manipulation of data. Databases make
data management easy.
Example:
a. An online telephone directory uses a database to store data of people, phone
numbers, and other contact details.
b. Your electricity service provider uses a database to manage billing,
client-related issues, handle fault data, etc.
c. Let us also consider Facebook. It needs to store, manipulate, and present
data related to members, their friends, member activities, messages,
advertisements, and a lot more.
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10. Data Model
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Data Model gives us an idea that how the final system will look like after its
complete implementation. It defines the data elements and the relationships
between the data elements. Data Models are used to show how data is
stored, connected, accessed and updated in the database management
system.
Though there are many data models being used nowadays but the Relational
model is the most widely used model. Apart from the Relational model, there
are many other types of data model some of the Data Models in DBMS are:
Types of Database Models
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1. Hierarchical Model
2. Network Model
3. Entity-Relationship Model
4. Relational Model
5. Object-Oriented Data Model
6. Object-Relational Data Model
7. Flat Data Model
8. Semi-Structured Data Model
9. Associative Data Model
10. Context Data Model

11. 1. Hierarchical Model
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Hierarchical Model was the first DBMS model. This model organises the data in the
hierarchical tree structure. The hierarchy starts from the root which has root data and
then it expands in the form of a tree adding child node to the parent node. This model
easily represents some of the real-world relationships like food recipes, sitemap of a
website etc.
Example: We can represent the relationship between the shoes present on a
shopping website in the following way:
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12. Features of Hierarchical Model
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1. One-to-many relationship: The data here is organised in a tree-like structure where the
one-to-many relationship is between the data types. Also, there can be only one path from
parent to any node. Example: In the above example, if we want to go to the node sneakers we
only have one path to reach there i.e through men's shoes node.
2. Parent-Child Relationship: Each child node has a parent node but a parent node can have
more than one child node. Multiple parents are not allowed.
3. Deletion Problem: If a parent node is deleted then the child node is automatically deleted.
4. Pointers: Pointers are used to link the parent node with the child node and are used to
navigate between the stored data. Example: In the above example the 'shoes' node points to the
two other nodes 'women shoes' node and 'men's shoes' node.
Advantages of Hierarchical Model
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1. It is very simple and fast to traverse through a tree-like structure.
2. Any change in the parent node is automatically reflected in the child node so, the
integrity of data is maintained.
Disadvantages of Hierarchical Model
1. Complex relationships are not supported.
2. As it does not support more than one parent of the child node so if we have some
complex relationship where a child node needs to have two parent node then that can't
be represented using this model.
3. If a parent node is deleted then the child node is automatically deleted.

13. 2. Network Model
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This model is an extension of the hierarchical model. It was the most popular
model before the relational model. This model is the same as the hierarchical
model, the only difference is that a record can have more than one parent. It
replaces the hierarchical tree with a graph.
Example: In the example below we can see that node student has two
parents i.e. CSE Department and Library. This was earlier not possible in
the hierarchical model.
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14. Features of Network Model
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1. Ability to Merge more Relationships: In this model, as there are more
relationships so data is more related. This model has the ability to manage
one-to-one relationships as well as many-to-many relationships.
2. Many paths: As there are more relationships so there can be more than one
path to the same record. This makes data access fast and simple.
3. Circular Linked List: The operations on the network model are done with the
help of the circular linked list. The current position is maintained with the help of
a program and this position navigates through the records according to the
relationship.
Advantages of Network Model
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1. The data can be accessed faster as compared to the hierarchical model. This is because the data
is more related in the network model and there can be more than one path to reach a particular
node. So the data can be accessed in many ways.
2. As there is a parent-child relationship so data integrity is present. Any change in parent record
is reflected in the child record.
Disadvantages of Network Model
1. As more and more relationships need to be handled the system might get complex. So,
a user must be having detailed knowledge of the model to work with the model.
2. Any change like updation, deletion, insertion is very complex.

15. 3. Entity-Relationship Model
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Entity-Relationship Model or simply ER Model is a high-level
data model diagram. In this model, we represent the real-world
problem in the pictorial form to make it easy for the stakeholders
to understand. It is also very easy for the developers to understand
the system by just looking at the ER diagram.
We use the ER diagram as a visual tool to represent an ER Model.
ER diagram has the following three components:
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● Entities: Entity is a real-world thing. It can be a person, place, or
even a concept. Example: Teachers, Students, Course, Building,
Department, etc are some of the entities of a School Management
System.
● Attributes: An entity contains a real-world property called attribute.
This is the characteristics of that attribute. Example: The entity
teacher has the property like teacher id, salary, age, etc.
● Relationship: Relationship tells how two attributes are related.
Example: Teacher works for a department

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Features of of ER Model
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● Graphical Representation for Better Understanding: It is very
easy and simple to understand so it can be used by the developers to
communicate with the stakeholders.
● ER Diagram: ER diagram is used as a visual tool for representing
the model.
● Database Design: This model helps the database designers to build
the database and is widely used in database design.

17. Advantages of ER Model
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● Simple: Conceptually ER Model is very easy to build. If we know the
relationship between the attributes and the entities we can easily build the ER
Diagram for the model.
● Effective Communication Tool: This model is used widely by the database
designers for communicating their ideas.
● Easy Conversion to any Model: This model maps well to the relational model
and can be easily converted relational model by converting the ER model to the
table. This model can also be converted to any other model like network model,
hierarchical model etc.
Disadvantages of ER Model
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● No industry standard for notation: There is no industry
standard for developing an ER model. So one developer might
use notations which are not understood by other developers.
● Hidden information: Some information might be lost or
hidden in the ER model. As it is a high-level view so there are
chances that some details of information might be hidden.

18. 4. Relational Model
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Relational Model is the most widely used model. In this model,
the data is maintained in the form of a two-dimensional table. All
the information is stored in the form of row and columns. The
basic structure of a relational model is tables. So, the tables are
also called relations in the relational model. Example: In this
example, we have an Employee table.
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19. Features of of Relational Model
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● Tuples: Each row in the table is called tuple. A row contains all the
information about any instance of the object. In the above example,
each row has all the information about any specific individual like the
first row has information about John.
● Attribute or field: Attributes are the property which defines the table
or relation. The values of the attribute should be from the same
domain. In the above example, we have different attributes of the
employee like Salary, Mobile_no, etc.
Advantages of Relational Model
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● Simple: This model is more simple as compared to the network and
hierarchical model.
● Scalable: This model can be easily scaled as we can add as many
rows and columns we want.
● Structural Independence: We can make changes in database
structure without changing the way to access the data. When we can
make changes to the database structure without affecting the
capability to DBMS to access the data we can say that structural
independence has been achieved.

20. Disadvantages of Relational Model
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● Hardware Overheads: For hiding the complexities and making things easier
for the user this model requires more powerful hardware computers and data
storage devices.
● Bad Design: As the relational model is very easy to design and use. So the users
don't need to know how the data is stored in order to access it. This ease of
design can lead to the development of a poor database which would slow down
if the database grows.
But all these disadvantages are minor as compared to the advantages of the
relational model. These problems can be avoided with the help of proper
implementation and organisation.
5. Object-Oriented Data Model
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In this model, both the data and relationship are present in a single
structure known as an object. We can store audio, video, images,
etc in the database which was not possible in the relational
model(although you can store audio and video in relational
database, it is advised not to store in the relational database). In
this model, two are more objects are connected through links.
We use this link to relate one object to other objects. This can
be understood by the example given below.

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Advantages of Object-Oriented Model
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• Object database can handle different types of data while relational
database handles a single data. Unlike traditional databases like
hierarchical, network or relational, the object-oriented databases can
handle the different types of data, for example, pictures, voice video,
including text, numbers and so on.
• Object-oriented databases provide us code reusability, real world
modelling, and improved reliability and flexibility.
• The object-oriented database is having low maintenance costs as
compared to other model because most of the tasks within the system are
encapsulated, they may be reused and incorporated into new tasks.

22. Disadvantages of Object-Oriented Model
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• There is no universally defined data model for an OODBMS, and
most models lack a theoretical foundation.
• In comparison to RDBMSs the use of OODBMS is still relatively
limited.
• There is a Lack of support for security in OODBMSs that do not
provide adequate security mechanisms.
• The system more complex than that of traditional DBMSs.
6. Object-Relational Data Model
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As the name suggests it is a combination of both the relational
model and the object-oriented model. This model was built to fill
the gap between object-oriented model and the relational model.
We can have many advanced features like we can make complex
data types according to our requirements using the existing data
types. The problem with this model is that this can get complex
and difficult to handle.
Relational databases common in programming languages like
C++, C#, and Java.

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7. Flat Data Model
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It is a simple model in which the database is
represented as a table consisting of rows and
columns. To access any data, the computer has to
read the entire table. This makes the modes slow
and inefficient.

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8. Semi Structured Data Model
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The semi-structured data model is a generalized form of the relational
model, which allows representing data in a flexible way, hence we can
not differentiate between data and schema in this model because, in this
model, some entities have a missing attribute(s) and on the other hand,
some entities might have some extra attribute(s) which in turn makes it
easy to update the schema of the database.
For example - We can say a data model to be semi-structured if in
some attributes we are storing both atomic values (values that can't
be divided further, for example, Roll_No) as well as a collection of
values.

25. 9. Associative Data Model
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The associative data model sees the data in the same way as the brain does, i.e.
entities and relationships between them. The relationship is expressed as a
simple English sentence of the form "subject-verb-object". For example - From
the sentences
● Pulkit is a customer
● Pulkit's customer id is 645.
● Neeraj is a customer
● Neeraj's customer id is 784.
10. Context Data Model
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The context model is nothing but a combination of several data
models that have been discussed above.
For example, a context model can be a combination of a network
model, ER model, etc. This data model allows one to do many
things which were not possible if he/she use a single data model.

26. Database Management System
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A database management system (DBMS) is system
software for creating and managing databases. A DBMS
makes it possible for end users to create, protect, read,
update and delete data in a database. The most prevalent
type of data management platform, the DBMS essentially
serves as an interface between databases and users or
application programs, ensuring that data is consistently
organized and remains easily accessible.
Database Management System Example
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Let us see a simple example of a university database. This database is
maintaining information concerning students, courses, and grades in a
university environment. The database is organized as five files:
● The STUDENT file stores the data of each student
● The COURSE file stores contain data on each course.
● The SECTION stores information about sections in a particular course.
● The GRADE file stores the grades which students receive in the various
sections
● The TUTOR file contains information about each professor.

27. Features of DBMS
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● Data Normalization
The risk of data duplication in a database is relatively high as multiple users share it simultaneously. Data
normalization mitigates this risk and minimizes the chance of destructive anomalies appearing. No data
redundancy and repetition save storage and significantly improve access time.
● User-defined rules and constraints
Referential Integrity constraints help organizations prevent accidental damage to the database by
authorized users. A database management software allows users to define validation and integrity rules
and conditions to ensure data satisfies the semantics.
● Security protocols
Security controls protect the integrity of a database and the data and records residing in it. Some essential
DBMS security controls include data encryption, user authentication, and user authorization.
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● Data backup
A backup protects your database against data loss. A copy of files stored in a database must be available to
reconstruct data in case data get lost or corrupted. Most DBMS support logical and physical data backup.
● Data structuring
A DBMS must allow users to organize information in a database in a clear hierarchical structure. It means
all objects, records, and tables can be arranged systematically, like a catalog, so the records can easily be
accessed and retrieved.
In addition, you must also look for various features and functionality depending on your use case. For
example, a DBMS should offer easy database customization options, support multi-user access, and
contain a metadata library.

28. Applications of Database Management Software
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Following are some of the applications of database software in different industries:
● Banks: In the banking sector, DBMS is used to store client info, account activities, disbursements,
credits, and mortgages
● Airlines: Flight bookings and scheduling info is stored in databases.
● Education: Student information, course registrations, and results are accumulated in database
systems.
● Telecommunication: Databases store call archives, monthly bills, retaining balances, and other
call-related information.
● Economics and Finance: DBMS stores data about bonds, transactions, and acquisitions of fiscal
instruments, such as shares and stocks.
● Sales and Marketing: Prospect and customer information is stored and accessed via databases.
● Human Resources: Records about workers, remunerations, payroll, deduction, generating salaries,
and more information are kept in database systems.
File Management system
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A file management system is a cloud-based (online) method of
storing, organizing, and managing access to information. A good
file management system is efficient, stores information in a
centralized location, and makes it easier to find office files by
providing a searchable database for quick retrieval.

29. Difference between File System and DBMS
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30. Advantages of Database Management System
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● Redundancy problem can be solved.
In the File System, duplicate data is created in many places because all the programs have their own files which
create data redundancy resulting in wastage of memory. In DBMS, all the files are integrated in a single database.
So there is no chance of duplicate data.
For example: A student record in a library or examination can contain duplicate values, but when they are
converted into a single database, all the duplicate values are removed.
● Has a very high security level.
Data security level is high by protecting your precious data from unauthorized access. Only authorized users
should have the grant to access the database with the help of credentials.
● Presence of Data integrity.
Data integrity makes unification of so many files into a single file. DBMS allows data integrity which makes it
easy to decrease data duplicity Data integration and reduces redundancy as well as data inconsistency.
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● Support multiple users.
DBMS allows multiple users to access the same database at a time without any conflicts.
● Avoidance of inconsistency.
DBMS controls data redundancy and also controls data consistency. Data consistency is nothing but if
you want to update data in any files then all the files should not be updated again.
In DBMS, data is stored in a single database so data becomes more consistent in comparison to file
processing systems.
● Shared data
Data can be shared between authorized users of the database in DBMS. All the users have their own right
to access the database. Admin has complete access to the database. He has a right to assign users to access
the database.

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● Enforcement of standards
As DBMS have central control of the database. So, a DBA can ensure that all the applications
follow some standards such as format of data, document standards etc. These standards help
in data migrations or in interchanging the data.
● Any unauthorized access is restricted
Unauthorized persons are not allowed to access the database because of security credentials.
● Provide backup of data
Data loss is a big problem for all the organizations. In the file system users have to back up
the files in regular intervals which lead to waste of time and resources.
Disadvantages of Database Management System
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● Complexity: The provision of the functionality that is expected of a good DBMS
makes the DBMS an extremely complex piece of software. Database designers,
developers, database administrators and end-users must understand this
functionality to take full advantage of it.
● Size: The functionality of DBMS makes use of a large piece of software which
occupies megabytes of disk space.
● Performance: Performance may not run as fast as desired.
● Higher impact of a failure: The centralization of resources increases the
vulnerability of the system because all users and applications rely on the
availability of DBMS, the failure of any component can bring operation to halt.
● Cost of DBMS: The cost of DBMS varies significantly depending on the
environment and functionality provided. There is also the recurrent annual
maintenance cost.

32. Data Warehousing
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The data warehouse (DWH) is a repository where an organization electronically
stores data by extracting it from operational systems, and making it available for
ad-hoc queries and scheduled reporting. In contrast, the process of building a data
warehouse entails designing a data model that can quickly generate insights.
Data stored in the DWH is different from data found in the operational
environment. It is organized so that relevant data is clustered together to facilitate
day-to-day operations, analysis, and reporting. This helps determine the trends over
time and allows users to create plans based on that information. Hence, reinforcing
the importance of data warehouse use in businesses.
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33. Examples of Data Warehousing
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● Investment and Insurance sector
A data warehouse is primarily used to analyze customer and market trends and other data patterns in the
investment and insurance sector. Forex and stock markets are two major sub-sectors where data warehouses play
a crucial role because a single point difference can lead to massive losses across the board. DWHs are usually
shared in these sectors and focus on real-time data streaming.
● Retail chains
DWHs are primarily used for distribution and marketing in the retail sector to track items, examine pricing
policies, keep track of promotional deals, and analyze customer buying trends. Retail chains usually incorporate
EDW systems for business intelligence and forecasting needs.
● Healthcare
A DWH is used to forecast outcomes, generate treatment reports, and share data with insurance providers,
research labs, and other medical units in the healthcare sector. EDWs are the backbone of healthcare systems
because the latest, up-to-date treatment information is crucial for saving lives.
Types of Data Warehousing
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1- Enterprise Data Warehouse
Enterprise data warehouse (EDW) serves as a central or main database to facilitate decision-making throughout
the enterprise. Key benefits of having an EDW include access to cross-organizational information, the ability to
run complex queries, and the enablement of enriched, far-sighted insights for data-driven decisions and early risk
assessment.
2- ODS (Operational Data Store)
In ODS, the DWH refreshes in real-time. Therefore, organizations often use it for routine enterprise activities,
such as storing records of the employees. Business processes also use ODS as a source for providing data to the
EDW.
3- Data Mart
It is a subset of a DWH that supports a particular department, region, or business unit. Consider this: You have
multiple departments, including sales, marketing, product development, etc. Each department will have a central
repository where it stores data. This repository is called a data mart.

34. Data Mining
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The practice of categorizing raw datasets into patterns based on trends or
abnormalities is known as data mining. Companies utilize a variety of data
mining methods and tactics to gather information for data analytics and deeper
business insights.
For modern firms, data is the most valuable asset. Extracting important data from a
disorganized data source is tough, similar to mining gold. For data patterns or
trends, you'll need to employ tools. Data is not completely erased from a data
collection, unlike minerals. This procedure entails defining the structure of a data
collection, the connections between the various data, and what data to extract for
data analysis.
Data Mining Process
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The data mining process can be broken down into these four primary stages:
1. Data gathering. Relevant data for an analytics application is identified and
assembled. The data may be located in different source systems, a data warehouse
or a data lake, an increasingly common repository in big data environments that
contain a mix of structured and unstructured data. External data sources may also
be used. Wherever the data comes from, a data scientist often moves it to a data
lake for the remaining steps in the process.
2. Data preparation. This stage includes a set of steps to get the data ready to be
mined. It starts with data exploration, profiling and pre-processing, followed by
data cleansing work to fix errors and other data quality issues. Data transformation
is also done to make data sets consistent, unless a data scientist is looking to
analyze unfiltered raw data for a particular application.

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3. Mining the data. Once the data is prepared, a data scientist chooses the appropriate
data mining technique and then implements one or more algorithms to do the mining. In
machine learning applications, the algorithms typically must be trained on sample data
sets to look for the information being sought before they're run against the full set of
data.
4. Data analysis and interpretation. The data mining results are used to create
analytical models that can help drive decision-making and other business actions. The
data scientist or another member of a data science team also must communicate the
findings to business executives and users, often through data visualization and the use of
data storytelling techniques.
Advantages and Disadvantages of Data Mining
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MS Access
Introduction
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● Microsoft Access is a well-known database management system produced by Microsoft
and is part of the Microsoft 365 office suite. Microsoft Access combines Microsoft’s
relational Jet Database Engine with software development tools and a graphic user
interface (GUI). It was first released in November 1992, so it’s been around for a while.
In the rapidly changing, fast-paced IT world, we can best describe a 30-year-old
program as "venerable."
● Microsoft Access also has the distinction of being the first mass-market database
program for Windows.
● Microsoft Access enables business and enterprise users to manage data and analyze vast
amounts of information efficiently. The program provides a blend of database
functionality and programming capabilities for creating easy-to-navigate forms.
● Microsoft Access is like Microsoft Excel in that you can store, edit, and view data.
However, Access has much more to offer, as we are about to see.

37. Meaning of MS Access
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Microsoft Access is a relational database program and a typical
database includes tables, queries, forms, and reports. With
Microsoft Access, you can easily organize, store and retrieve
data. There are several benefits or advantages to using
Microsoft Access.
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Components of MS Access
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● Tables
It is the main component of the MS Access software. In the MS Access database, tables are mainly used for storing
the data or information in the form of rows and columns.
The Access tables which contain the data or information look similar to the tables in MS Excel or MS Word.
Whenever, you create a new database in MS Access, firstly, you have to create a table in that database. You can
also relate a specific table to other tables, and easily define the primary key in that table.
● Relationships
Relationships are the links or connections, which are formed between the one or more tables in the database. There
exist following four types of relationships:
1. One-to-One Relationship
2. One-to-Many Relationship
3. Many-to-One Relationship
4. Many-to-Many Relationship

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● Queries
Queries are the commands, which are used to retrieve the data or information from the database. It also
allows you to insert the information in the MS Access database.
● Forms
It is an object or a component, which helps the users for entering the data in the table of any database by an
interface. Any user can easily display the data of the database.
● Reports
When the users inserted the data in the database, then they can easily view their information in an
organized manner by running the reports. Unlike forms, the reports cannot be edited.
● Macros
Macros are used for performing the repetitive tasks on reports and forms in the MS Access database. It also
allows the user for adding functionalities to forms, controls, and reports.
● Modules
Modules are used to perform the automating routine operations and user-defined functions which are
written in VBA. Any user can easily use these modules from anywhere in the MS Access database.
Benefits of MS Access
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● Easy to create database within lesser time duration
● Used a very comprehensive programming language
which made it user friendly
● With each revised version, new options and features were
made available to the users for their convenience
● It is easy to install and then easy to understand its
working
● Importing data was easy
● Graphical user interface made it easy to use

40. Limitations of MS Access
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● Not too many people can use the same database at a single time.
This may affect its speed and efficiency
● The same database was tough to use with different Operating
systems
● Better database systems can be used for confidential data
Difference between MS Excel and MS Access
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How to create Database in MS Access
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43. How to Create Table in MS Access
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How to add Data in a Database
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How to Edit Data in a Database
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How to Delete Data in a Database
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How to Create Forms in a MS Access
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49. How to Create Reports in a MS Access
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