UNIT – I
INTRODUCTION TO DATABASE MANAGEMENT SYSTEM
An overview of database management system –
 What is Data?
Data is nothing but facts and statistics stored or free flowing over a network,
generally it's raw and unprocessed. For example: When you visit any website, they
might store you IP address, that is data, in return they might add a cookie in your
browser, marking you that you visited the website, that is also data.
Data becomes information when it is processed, turning it into something
meaningful. Like, based on the cookie data saved on user's browser, if a website
can analyze that generally men of age 20-25 visit us more, that is information,
derived from the data collected.
 What is a Database?
A Database is a collection of related data organized in a way that data can be
easily accessed, managed and updated. Database can be software based or
hardware based, with one sole purpose, storing data.
During early computer days, data was collected and stored on tapes, which were
mostly write-only, which means once data is stored on it, it can never be read
again. They were slow and bulky, and soon computer scientists realized that they
needed a better solution to this problem.
 What is DBMS and its Types?
A collection of information which is managed such that it can be updated and
easily accessed is called a database. A software package which can be used to
manipulate, validate and retrieve this database is called a Database Management
System.
For example, Airlines use this software package to book tickets and confirm
reservations which are then managed to keep a track of the schedule.
In other words, A DBMS is a software that allows creation, definition and
manipulation of database, allowing users to store, process and analyze data easily.
DBMS provides us with an interface or a tool, to perform various operations like
creating database, storing data in it, updating data, creating tables in the database
and a lot more.
DBMS also provides protection and security to the databases. It also maintains data
consistency in case of multiple users.
Here are some examples of popular DBMS used these days:






MySql
Oracle
SQL Server
IBM DB2
PostgreSQL
Amazon SimpleDB (cloud based) etc.
There are majorly four types of databases:




Network Database:When the details of multiple members can be linked to
the files of multiple owners and vice versa, it is called a network database.
Hierarchical Database:When the data stored in the form of records and is
connected to each other through links is called hierarchical database. Each
record comprises fields and each field comprises only one value.
Relational Database:When the data is organized as a set of tables
comprising rows and columns with a pre-defined relationship with one
another, it is called a relational database.
Object-oriented Database– the information is represented as objects, with
different types of relationships possible between two or more objects. Such
databases use an object-oriented programming language for development.
 Components of Database Management Software:
There four main components on which the working of a DBMS depends. This
includes:
1. Data:The main component is the data. The entire database is set based on
the data and the information processed based on it. This data acts as a bridge
between the software and hardware components of DBMS. This can further
be divided into three varieties:

User Data –The actual data based on which the work is done

Metadata –This is the data of the data, i.e., managing the data
required to enter the information

Application MetaData –This is the structure and format of the
queries
To simplify it, in a table, the information given in each table is the User Data, the
number of tables, rows and columns is the MetaData the structure we choose is the
Application MetaData.
2. Hardware:These are the general hardware devices which help us save and
enter the data like hard disks, magnetic tapes, etc.
3. Software:The software acts as a medium of communication between the
user and the database. Based on the user’s requirement, the database can be
modified and updated. To perform operations on the data, query languages
like SQL are used.
4. Users:No function can be performed without the Users. Thus, they form the
fourth most important component of DBMS. The information entered into a
database is used by the User or the administrator to perform their business
operations and responsibilities.
 Characteristics of Database Management System:
A database management system has following characteristics:
1. Data stored into Tables:Data is never directly stored into the database. Data
is stored into tables, created inside the database. DBMS also allows to have
relationships between tables which makes the data more meaningful and
connected. You can easily understand what type of data is stored where by
looking at all the tables created in a database.
2. Reduced Redundancy:In the modern world hard drives are very cheap, but
earlier when hard drives were too expensive, unnecessary repetition of data
in database was a big problem. But DBMS follows Normalization which
divides the data in such a way that repetition is minimum.
3. Data Consistency:On Live data, i.e. data that is being continuously updated
and added, maintaining the consistency of data can become a challenge. But
DBMS handles it all by itself.
4. Support Multiple user and Concurrent Access:DBMS allows multiple users
to work on it (update, insert, delete data) at the same time and still manages
to maintain the data consistency.
5. Query Language:DBMS provides users with a simple Query language, using
which data can be easily fetched, inserted, deleted and updated in a database.
6. Security:The DBMS also takes care of the security of data, protecting the
data from un-authorized access. In a typical DBMS, we can create user
accounts with different access permissions, using which we can easily secure
our data by restricting user access.
7. DBMS supports transactions, which allows us to better handle and manage
data integrity in real world applications where multi-threading is extensively
used.
 Advantages of DBMS:
1.
Segregation of application program.
2.
Minimal data duplicacy or data redundancy.
3.
Easy retrieval of data using the Query Language.
4.
Reduced development time and maintenance need.
5.
With Cloud Data centers, we now have Database Management Systems
capable of storing almost infinite data.
6.
Seamless integration into the application programming languages which
makes it very easier to add a database to almost any application or website.
 Disadvantages of DBMS:
1.
It's Complexity
2.
Except MySQL, which is open source, licensed DBMSs are generally costly.
3. They are large in size.
 What is a File System?
The operating system’s file system is a mechanism and data structure for
controlling how data is saved and accessed. Data deposited in a storage medium
without a file system would be one huge body of data with no way of knowing
where one piece of data ends and the next begins. The data is easily extracted and
identified by splitting it into bits and giving each one a name. Each group of data is
referred to as a “file”.
A “file system” is the structure and logic rules that are used to organize groups of
data and their names. Files can be collectively present inside a directory.
Directories can again be present inside another directory giving it a hierarchical
structure. Although Windows supports a variety of file systems, NTFS is the most
popular in today’s world. Because files are commonly organized in a hierarchy, the
file system allows you to browse the files in the current directory.
 Database System versus File System:
Key difference between File System and DBMS 




A file system is software that manages and organizes the files in a storage
medium, whereas a DBMS is a software application that is used for
accessing, creating, and managing databases.
The file system doesn’t have a crash recovery mechanism; on the other
hand, the DBMS provides a crash recovery mechanism.
Data inconsistency is higher in the file system. On the contrary, data
inconsistency is low in a database management system.
File system does not provide support for complicated transactions, while in
the DBMS system, it is easy to implement complicated transactions using
SQL.
File system does not offer concurrency, whereas the DBMS provides a
concurrency facility.
Parameters
DBMS
File System
Structure
Store the data as a
Stores the data as a
table, key-value format
hierarchical structure
or a graph.
where files are
children of directories
(parent).
Data
Performs normalization
No way to refer the data
Redundancy
and referencing to
in file
avoid
system, hence causing
redundancy.
redundancy.
Backup and
Allows for automated
Usually have to perform
Recovery
backup and recovery of
manual backup and
the data.
recovery operations
Query
Queries process faster
Queries usually take time
Processing
due to efficient data
due to unavailability of
structures and
advanced fetching
indexing, caching
algorithms.
support.
Consistency
Complexity
It has ACID properties
Lacks any mechanism for
which help in data
data
consistency.
consistency.
More complex due to a
Simpler than DBMS due to
lot of
less
features and data
number of features.
management methods.
Cost
Cost is high due to a lot
Less costly due to simpler
of
features.
advanced features like
distributed computing,
load
balancing, etc.
What is Database Architecture?
A Database Architecture is a representation of DBMS design. It helps to design,
develop, implement, and maintain the database management system. A DBMS
architecture allows dividing the database system into individual components that
can be independently modified, changed, replaced, and altered. It also helps to
understand the components of a database.
A Database stores critical information and helps access data quickly and securely.
Therefore, selecting the correct Architecture of DBMS helps in easy and efficient
data management.
Types of DBMS Architecture:
There are mainly three types of DBMS architecture:



One Tier Architecture (Single Tier Architecture)
Two Tier Architecture
Three Tier Architecture
1-Tier Architecture1 Tier Architecture in DBMS is the simplest architecture of Database in which the
client, server, and Database all reside on the same machine. A simple one tier
architecture example would be anytime you install a Database in your system and
access it to practice SQL queries. But such architecture is rarely used in
production.
2-Tier Architecture-
A 2 Tier Architecture in DBMS is a Database architecture where the presentation
layer runs on a client (PC, Mobile, Tablet, etc.), and data is stored on a server
called the second tier. Two tier architecture provides added security to the DBMS
as it is not exposed to the end-user directly. It also provides direct and faster
communication.
In the above 2 Tier client-server architecture of database management system, we
can see that one server is connected with clients 1, 2, and 3.
Two Tier Architecture Example:
A Contact Management System created using MS- Access.
3-Tier ArchitectureA 3-Tier Architecture in DBMS is the most popular client server architecture in
DBMS in which the development and maintenance of functional processes, logic,
data access, data storage, and user interface is done independently as separate
modules. Three Tier architecture contains a presentation layer, an application layer,
and a database server. 3-Tier database Architecture design is an extension of the 2tier client-server architecture.
A 3-tier architecture has the following layers:
1. Presentation layer (your PC, Tablet, Mobile, etc.)
2. Application layer (server)
3. Database Server
The Application layer resides between the user and the DBMS, which is
responsible for communicating the user’s request to the DBMS system and send
the response from the DBMS to the user. The application layer (business logic
layer) also processes functional logic, constraint, and rules before passing data to
the user or down to the DBMS.
The goal of Three Tier client-server architecture is:




To separate the user applications and physical database
To support DBMS characteristics
Program-data independence
Supporting multiple views of the data
Three Tier Architecture Example:
Any large website on the internet.
 What are Data Models in DBMS?
The Data Model gives us an idea of how the final system would look after it has
been fully implemented. It specifies the data items as well as the relationships
between them. In a database management system, data models are often used to
show how data is connected, stored, accessed, and changed. We portray the
information using a set of symbols and language so that members of an
organization may understand and comprehend it and then communicate.
 Types of Data Models:
Though there are other data models in use today, the Relational model is the most
used. Aside from the relational model, there are a variety of different data models
that we shall discuss in-depth in this article. Data Models in DBMS include the
following:
1. Hierarchical Model
This concept uses a hierarchical tree structure to organize the data. The hierarchy
begins at the root, which contains root data, and then grows into a tree as child
nodes are added to the parent node.
The Hierarchical Model was the first database management system model. This
concept uses a hierarchical tree structure to organize the data. The hierarchy begins
at the root, which contains root data, and then grows into a tree as child nodes are
added to the parent node. This model accurately depicts several real-world
relationships such as food recipes, website sitemaps, and so on.
Example: The following diagram depicts the relationship between the shoes
available on a shopping website:
Features of a Hierarchical Model:
Parent – Child Relationship A parent node exists for each child node. However, a parent node might have
several child nodes. It is not permitted to have more than one parent.
One -to-Many Relationship The data is organized in a tree-like form, with the datatypes having a one-to-many
relationship. There can only be one path from any node to its parent. For example,
in the preceding example, there is only one way to get to the node ‘sneakers’,
which is through the ‘men’s shoes’ node.
Deletion Problem When a parent node is removed, the child node is removed as well.
Pointers Pointers are used to connect the parent and child nodes and to traverse and
navigate between the stored data. The ‘shoes’ node in the above example points to
the two additional nodes, ‘women’s shoes’ and ‘men’s shoes.’
Pros of Hierarchical Model:


A tree-like structure is incredibly straightforward and quick to navigate.
Any modification to the parent node is reflected automatically in the child
node, ensuring data integrity.
Cons of Hierarchical Model:



Relationships that are complex are not supported.
Because it only supports one parent per child node, if we have a complex
relationship in which a child node needs to have two parents, we won’t be
able to describe it using this model.
When a parent node is removed, the child node is removed as well.
2. Network Model:
The hierarchical model is extended in the network model. Prior to the relational
model, it was the most popular model. To increase database performance and
standards, the network model was devised to express complicated data
relationships more effectively than hierarchical models. It has entities that are
grouped in a graphical format, and some of the entities can be reached by many
paths.
Example: We can observe that the node student has two parents, CSE Department
and Library, in the example below. In the hierarchical model, this was previously
impossible.
Features of a Network Model: Multiple Paths There may be several paths to the same record due to the increased number of
relationships. It allows for quick and easy data access.
The Ability to merge more Relationships Data is more connected in this model since there are more relationships. This
paradigm can handle many-to-many as well as one-to-one relationships.
Circular Linked List The circular linked list is used to perform operations on the network model. The
present position is kept up to date with the help of a software, and it navigates
through the records based on the relationship.
Pros of Network Model 

In comparison to the hierarchical model, data can be retrieved faster. This is
because the data in the network model is more related, and there may be
more than one path to a given node. As a result, the data can be accessed in a
variety of ways.
Data integrity is present since there is a parent-child relationship. Any
changes to the parent record are mirrored in the child record.
Cons of Network Model 
As the number of relationships to be managed grows, the system may get
increasingly complicated. To operate with the model, a user must have a
thorough understanding of it.

Any alteration, such as an update, deletion, or insertion, is extremely
difficult.
3. Entity – Relationship Model:
In the ER (Entity-Relationship) database model, real-world objects of interest are
represented as entities, while their properties or details are captured as attributes.
Relationships between these entities are established to show how they are
connected.
ER models use diagrams (ER diagrams) to visually represent these entities and
relationships, making it easier for stakeholders—such as developers, analysts, and
clients—to understand the database structure.
For example, when designing a school database, a Student can be an entity with
attributes like name, age, and address. Since an address is often a complex
structure, it can be modeled as a separate entity with its own attributes such as
street, pincode, and city. A relationship is then defined between the Student and
Address entities.
ER diagrams also support different types of relationships (e.g., one-to-one, one-tomany, many-to-many), which further help in modeling real-world scenarios
accurately. To explore more, you can study ER diagrams in detail to understand how
entities and relationships work together.
Advantages of the ER Model
 The ER (Entity-Relationship) model is simple to understand and easy to
design, making it accessible to both technical and non-technical users.
 It provides a clear and logical way to represent data structures visually.
 Although the ER model is not directly implemented in databases, it serves as
a blueprint for designing relational database models.
4. Relational Model
The relational model organizes data into two-dimensional tables, known as
relations, where each table represents a specific type of data. Relationships
between tables are maintained through common fields (also called foreign keys).
Introduced by E.F. Codd in 1970, this model has become the most widely used
approach to database management.
 Each table contains rows (records) and columns (attributes), with all data
related to a specific entity type stored within a single table.
 You can design, structure, and normalize these tables to reduce data
redundancy and improve consistency.
 Data is accessed and manipulated using SQL (Structured Query
Language).
 Popular relational databases include Oracle, MySQL, PostgreSQL, and
Microsoft.
Advantages of the Relational Model
 The relational model is straightforward and easy to implement, making it
ideal for a wide range of applications.
 A variety of popular database software—such as Oracle, MySQL,
PostgreSQL, and SQL Server—are built on this model.
 It supports SQL (Structured Query Language), allowing users to efficiently
query, manipulate, and manage data.
5. Object-Oriented Model
In the object-oriented database model, data is stored in the form of objects,
similar to how it's structured in object-oriented programming (OOP) languages
like Java or C++.
 Objects in this model encapsulate both data (attributes) and behavior
(methods), making it ideal for applications with complex data structures.
 The behavior of this model closely aligns with OOP principles, promoting
better data reusability and modularity.
 A widely known example of an Object Database Management System
(ODBMS) is MongoDB, which is also categorized under NoSQL databases.
 However, the object-oriented model is less mature and less widely
adopted compared to the relational model, particularly in enterprise-level
applications.
Schema and Instances
The overall design of a database is referred to as the database schema. It
defines the logical structure of the database, including tables, relationships,
views, indexes, and other elements. Unlike the actual data stored in the database,
the schema serves as a blueprint and does not include the data itself.
Database schemas are generally stable and do not change frequently, as they
represent the foundational design of how data is organized and accessed.
Types of Database Schema
A database schema defines the structure, format, and organization of data in a
database. It specifies how data is stored, what type of data is stored, and how
different pieces of data relate to each other. While data may change frequently, the
schema typically remains stable unless explicitly modified.
There are three main types of database schemas:
1. Physical Schema
 Represents the physical storage of data on hardware (e.g., how data is
stored in files, indexes, memory, etc.).
 It is hidden beneath the logical schema and can be modified without
impacting application programs.
 Changes to the physical schema usually involve performance tuning or
changes in storage structure.
2. Logical Schema
 Defines the logical structure of the database, such as tables, relationships,
constraints, and data types.
 This is the level that programmers and database designers interact with
when creating and modifying the database structure.
 Applications are built based on this schema.
3. External Schema (View Schema)
 Represents the view-level schema or how individual users or applications
see the data.
 Different users can have customized views of the same data depending on
their roles or access requirements.
 It is the highest level of abstraction, and multiple external schemas can
exist for a single logical schema.
Example
A database schema for a person will have fields for name, email, phone and address
as shown below −
Student
Name Email Contact
Database Instance (Extension / Database State)
A database instance refers to the collection of data stored in a database at a
specific point in time. It represents the current state of the database and is also
known as the extension or database state.
Unlike the schema, which defines the structure of the database and remains
relatively constant, the instance is dynamic—it changes frequently as data is
added, updated, or deleted.
Example
A database instance for the Person database can be
(User1,emai.com,11345679,addr) So the person construct will contain their
individual entities in the attributes called as instance. This is shown below −
Student
Name Email
Phone no
Amit
kpsd@yasd.com
2343435
John
werwr@sdas.in
5345464
Riya
wefrwer@sdf.com 2342342
Data Abstraction:Database systems, built on complex data structures, utilize abstraction to improve
data retrieval efficiency and simplify user interactions. Abstraction works by hiding
unnecessary details and presenting only relevant information, making the database
easier to use and manage. This concept is fundamental in Database Management
Systems (DBMS), where data abstraction shields end-users from the underlying
complexities of data storage and processing.
For example, when purchasing shoes online, a customer typically selects options
such as brand, size, color, and material—while behind the scenes, intricate details
like manufacturing processes, material sourcing, and production costs are hidden.
This selective visibility helps users focus only on what’s important to them,
enhancing usability and the overall experience.
Levels of Data Abstraction in DBMS
Data abstraction in DBMS is structured into three layers:
 Physical (Internal) Level
 Logical (Conceptual) Level
 View (External) Level
Each layer plays a crucial role in managing data efficiently while shielding users
from unnecessary complexity.
Physical Level (Internal Level)
The physical level, also known as the internal level, is the lowest layer of data
abstraction in a DBMS. It deals with the actual storage of data on physical media
such as hard drives. At this level, the complex data structures and access methods
used to store and retrieve data are defined. Decisions about how data is organized
and stored—such as indexing, file formats, and compression—are made by the
database administrator (DBA).
Example:
When users interact with a database, they typically see data in the form of rows and
columns, as in a relational table. However, at the physical level, these tables are
stored as binary files on secure servers or data centers. The underlying structure
and storage mechanisms are hidden from users, ensuring data is efficiently
managed and securely maintained.
Logical Level or Conceptual Level
The logical (or conceptual) level defines the structure of data after it is extracted
from the physical or internal level. It acts like a blueprint, organizing raw data into
meaningful structures without considering how the end user will interact with it.
Example:
Suppose we have raw data about products (e.g., product ID, product name,
manufacturing date) and customers (e.g., customer ID, customer name, address).
At the logical level, this data would be organized into structured tables—such as a
Products table and a Customers table. Relationships between these tables, such
as which customer ordered which product, can also be defined using joins.
View Level or External Level
The view (or external) level is the highest level of data abstraction in a DBMS. It
defines how data is presented to the end user. Instead of accessing the entire
database, users see only the data relevant to their needs, typically based on queries
made through the front-end. This data can be presented in various formats—such as
tables, graphs, or pie charts. Different users can have different views of the same
underlying data, tailored to their roles or requirements.
Example:
Referring to the earlier example, if a customer wants to view their order history,
they will only see the list of products they have ordered. On the other hand, a shop
owner might need to view a comprehensive table showing all ordered products
along with the customer details for delivery purposes. Each user interacts with a
specific "view" of the same data, without accessing the entire database.
Conclusion
Data abstraction in DBMS refers to the process of hiding the complex details of data
storage and management from end users, making data access simpler and more
secure.
There are three levels of data abstraction in a DBMS:
1. Physical Level (Internal Level):
This is the lowest level where raw data is stored in files on physical storage
devices like hard drives. It deals with how data is actually saved in the
system.
2. Logical Level (Conceptual Level):
At this level, data from the physical layer is structured and organized into
meaningful formats, such as tables, without revealing how the data is stored.
3. View Level (External Level):
This is the highest level of abstraction. It presents data to end users based on
their specific needs or queries. The same data can be represented in various
formats, such as tables, graphs, or pie charts, offering different views to
different users.
This layered approach ensures efficient data management, user-friendly interaction,
and enhanced security.
Data Independence in DBMS
Data independence refers to the ability to modify the database schema at one level
without requiring changes at higher levels. This ensures that changes in how data is
stored or structured do not impact how users or applications interact with the data.
It is a key feature that simplifies database maintenance, supports system evolution,
and promotes efficient data management.
There are two main types of data independence:
1. Physical Data Independence:
This allows changes to the internal (physical) schema—such as altering
storage devices, file structures, or indexing methods—without affecting the
conceptual schema or user views. For example, moving data to a new hard
drive or optimizing storage will not impact how the data is logically organized
or accessed by users.
2. Logical Data Independence:
This allows modifications to the conceptual schema—such as adding new
fields or relationships—without affecting the external schema or application
programs. For instance, introducing a new column to a table should not
require changes to user interfaces or queries that don’t use that column.
Why It Matters:
Data independence helps decouple application logic from data storage and
structure. This flexibility makes the system easier to update, scale, and adapt over
time without disrupting users or requiring costly rewrites of application code.
How It Works
Data independence is made possible by the three-schema architecture of a
Database Management System (DBMS), which separates data into different
abstraction levels. Each level serves a specific purpose and isolates changes from
affecting the others unnecessarily.
1. External Level (View Level):
This is the highest level, where individual users or applications interact with the
database. It defines how users see the data, often limited to only what is relevant
to them (e.g., specific tables, fields, or reports).
2. Conceptual Level (Logical Level):
This middle level defines the overall logical structure of the database. It includes
data models, relationships, and constraints, independent of how the data is
physically stored.
3. Internal Level (Physical Level):
This is the lowest level, concerned with the physical storage of data. It deals with
file organization, indexing methods, storage devices, and other hardware-related
details.
How Data Independence Works:
 Physical Data Independence allows changes at the internal level (e.g.,
reorganizing files or changing storage hardware) without altering the
conceptual level.
 Logical Data Independence allows changes at the conceptual level (e.g.,
adding new tables or attributes) without affecting the external level or
application interfaces.
This layered architecture ensures that any modification at a lower level is properly
mapped to the next higher level, preserving consistency and preventing changes
from cascading upward. As a result, users and applications remain unaffected by
internal changes, making the system more robust and easier to maintain.
Types of Data Independence
Physical Data Independence
What it is:
Physical data independence is the ability to modify the internal schema—which
defines how data is physically stored—without affecting the conceptual schema
or external views. This means changes in storage methods or hardware do not
impact how the data is logically structured or how users access it.
Examples:
 Switching from HDDs to SSDs for faster data access.
 Changing the indexing strategy to improve query performance.
 Implementing data compression techniques to save storage space.
Benefit:
Physical data independence enables backend storage optimizations and hardware
upgrades without requiring changes to applications or user interactions,
ensuring flexibility and system efficiency.
Here’s a refined and structured version of your explanation for Logical Data
Independence:
Logical Data Independence
What it is:
Logical data independence is the ability to modify the conceptual schema—which
defines the logical structure of the database—without impacting the external
schemas or application programs. This means changes to the data model do not
require rewriting application logic or altering user interfaces.
Examples:
 Adding a new column (e.g., an email field) to a customer table without
affecting existing applications that only access the name and address fields.
 Creating a new relationship between tables without changing existing queries
that don’t rely on it.
Benefit:
Logical data independence allows the database structure to evolve over time—such
as adding new fields, tables, or relationships—without disrupting existing
applications. This ensures long-term maintainability and adaptability of the
system.
Overall Database Architecture:The architecture of a DBMS is typically layered as follows:
 Top Layer:
This layer includes various application interfaces that serve different types
of users:
 Naive users who interact through simple interfaces
 Application programs developed by programmers
 Query tools for sophisticated users who run complex queries
 Administration tools used by database administrators for managing
the system
 Middle Layers:
These layers contain the core components of the DBMS:
 Query Processor: Interprets and executes database queries
 Storage Manager: Handles the efficient storage, retrieval, and
updating of data
 Bottom Layer:
This is the disk storage layer, where the actual data is physically stored on
hardware devices.
Together, these layers work to provide efficient, secure, and user-friendly access to
relational data stored in the database.