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GNDU Question Paper-2023
BCA 3
rd
Semester
DATABASE MANAGEMENT SYSTEM
Time Allowed: Three Hours Maximum Marks: 75
Note: Attempt Five questions in all, selecting at least One question from each section. The
Fifth question may be attempted from any section. All questions carry equal marks.
SECTION-A
1. What are the different types of data models supported by database system? Explain
Network Model in detail.
2. Explain the following terms giving suitable example:
(a) Strong Entity
(b) Weak Entity
(c) Relationships
(d) Specialization
(e) Generalization.
SECTION-B
3. How BCNF performs better than Third normal form? Explain using example.
4. What is Concurrency Control? What are its potential problems? Explain the concept of
Lock-based Protocols in CC.
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SECTION-C
5.(a) Explain the concept of DCL. Define DCL statements using suitable examples.
(b) Explain the concept of SQL Intersection using example.
6. What do you mean by Database Triggers? Discuss its different types. Write the syntax
for creating a database trigger using different parameters.
SECTION-D
7. How Big Data is used to solve the problems of large database? Explain its use in Data
Analytics.
8. Differentiate between SQL and NoSQL. Which one is better to use ?Explain using examples.
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GNDU Answer Paper-2023
BCA 3
rd
Semester
DATABASE MANAGEMENT SYSTEM
Time Allowed: Three Hours Maximum Marks: 75
Note: Attempt Five questions in all, selecting at least One question from each section. The
Fifth question may be attempted from any section. All questions carry equal marks.
SECTION-A
1. What are the different types of data models supported by database system? Explain
Network Model in detail.
Ans: Turning the Question into a Fun Story
Imagine you’re at a Magical Library, where every book represents data, and every shelf
arrangement represents how that data is organized. The librarian, who is a Database
Wizard, introduces you to three types of magical arrangements (data models). These
arrangements are like blueprints or maps that define how the books (data) are stored,
accessed, and linked. Here’s the story of these data models:
1. Hierarchical Model (The Family Tree Library)
Imagine a family tree where each parent can have multiple children, but each child
has only one parent. In this library, the books are arranged like that family tree. The
"parent" book is on top, and its "child" books are directly below it.
2. Network Model (The Spider Web Library)
Now, think of a spider web, where each book can connect to multiple other books in
a network of relationships. It’s a flexible and interconnected system. We’ll dive
deeper into this one soon!
3. Relational Model (The Magical Grid Library)
Finally, there’s a magical grid where books are arranged in rows and columns, just
like a spreadsheet. Each row is a record (a specific book), and each column is a
property of that book, like its title, author, or year.
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Exploring the Network Model: The Spider Web Library
In the Spider Web Library, the Network Model allows you to connect books in complex
ways. Here’s how it works in a simple and engaging way:
What is the Network Model?
The Network Model organizes data in a graph structure, where entities (data points) are
called nodes, and their relationships are represented as edges. Imagine each book is a node,
and the strings connecting them are edges. Unlike the family tree (hierarchical model), each
node can have many parents and many children.
How Does the Spider Web Library Work?
1. Flexible Connections:
You can link any book to any other book. For example, if "Book A" is about biology
and "Book B" is about chemistry, and they both refer to "Book C" (a general science
book), they can connect freely.
2. Schema:
The schema in this library is like a detailed map showing how all the books are
connected. It uses something called records (nodes) and sets (edges).
3. Owner-Member Relationship:
Every connection has an owner (a primary book) and a member (a connected book).
For instance, in the connection "Science --> Physics," "Science" is the owner, and
"Physics" is the member.
Advantages of the Network Model
• Fast Access: Since books are interconnected, finding a related book is very quick.
• Complex Relationships: It handles complex relationships easily, like many-to-many
connections.
• Efficiency: The spider web design avoids redundant data storage.
Disadvantages of the Network Model
• Complexity: The spider web can get tangled, making it hard to manage.
• Maintenance: Adding new books or changing connections requires careful planning.
• Lack of Standardization: It's not as standardized as modern systems like the
relational model.
An Example of the Network Model
Let’s say you’re managing a library database for a university.
• You have Books (nodes).
• You also have Authors (nodes).
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• A book can have multiple authors, and an author can write multiple books.
The Network Model allows you to directly link Books and Authors using sets (relationships).
Comparison with Other Models
• Hierarchical Model: Imagine you wanted to find a book in a family tree. You’d have
to climb down each branch, which takes time.
• Relational Model: This works like a grid where you need a magic key (SQL query) to
pull out related books.
The Network Model, however, lets you jump directly between interconnected books,
making it both unique and powerful for certain scenarios.
This is how the Magical Library of Data Models works, with each model having its own
strengths and weaknesses. The Network Model, like a spider web, connects information in a
way that can handle complex relationships and dependencies efficiently!
Fun Tip to Remember: Think of the network model as your social media feed. Every profile
(node) is connected to others through tags, mentions, and follows (edges), forming a web of
connections.
2. Explain the following terms giving suitable example:
(a) Strong Entity
(b) Weak Entity
(c) Relationships
(d) Specialization
(e) Generalization.
Ans: The Story of DataLand: A Magical Kingdom
In the kingdom of DataLand, the inhabitants live in harmony, forming connections and
communities. Each of them plays a unique role in keeping the kingdom organized.
1. Strong Entity: The Independent Heroes
In DataLand, Strong Entities are like independent knights or wizards who stand on their
own. They don’t need anyone else to define them. They are so self-sufficient that they have
a unique identity key that everyone in the kingdom recognizes.
For example:
• Think of a student in a school. A student has their own Roll Number, which is unique
and doesn’t depend on anyone else.
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• In database terms: A Strong Entity has a primary key that uniquely identifies it.
Example:
Imagine a table of students:
Roll Number
Name
Class
101
Alice
3
102
Bob
3
Here, Roll Number is the unique identifier, making Student a Strong Entity.
2. Weak Entity: The Dependents
Weak Entities are like the loyal sidekicks in DataLand who need their heroes to define them.
These sidekicks don’t have a unique identity of their own—they depend on their hero’s key
to be recognized.
For example:
• A bank account transaction depends on the bank account. Without knowing the
bank account, you can’t figure out which transaction belongs to whom.
• In database terms: Weak Entities depend on a foreign key relationship with a Strong
Entity and are identified by a composite key (the hero’s key + their own attribute).
Example:
Imagine a table of transactions related to accounts:
Account Number
Transaction ID
Amount
5001
01
$200
5001
02
$150
Here, Transaction ID alone isn’t enough. You need Account Number + Transaction ID to
identify a specific transaction.
3. Relationships: The Bonds of Friendship
In DataLand, relationships are the friendships and connections between entities. These
bonds define how one entity interacts with another.
For example:
• A teacher teaches a student. This is a relationship between the Teacher entity and
the Student entity.
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• In database terms: Relationships are represented by foreign keys and can be one-to-
one, one-to-many, or many-to-many.
Example:
A Teacher-Student relationship:
Teacher ID
Student ID
T01
S101
T02
S102
This table connects teachers and students, showing who teaches whom.
4. Specialization: Unique Superpowers
Specialization in DataLand is when a general type of hero develops specific superpowers
based on their role. For instance, some heroes specialize as warriors, while others become
healers.
For example:
• An employee could specialize as a manager or an engineer.
• In database terms: Specialization means creating sub-entities with attributes unique
to them but inheriting common attributes from the parent entity.
Example:
Imagine an Employee table:
Emp ID
Name
Type
1
Alice
Manager
2
Bob
Engineer
Then, we can have specialized tables for Manager and Engineer: Manager Table:
Emp ID
Team Size
1
10
Engineer Table:
Emp ID
Skill
2
AI
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5. Generalization: Finding the Common Ground
Generalization is the opposite of specialization. It’s like discovering that different heroes
share a common ancestry or traits. Warriors, healers, and mages might all belong to the
general category of heroes.
For example:
• Both Car and Truck can be generalized into a single entity called Vehicle.
• In database terms: Generalization combines multiple entities into a higher-level
entity.
Example:
Imagine two separate tables for Car and Truck: Car Table:
Car ID
Model
C1
Sedan
Truck Table:
Truck ID
Load Capacity
T1
5 Tons
Using generalization, we can combine them into a single Vehicle table:
Vehicle ID
Type
Attribute 1
Attribute 2
V1
Car
Model: Sedan
NULL
V2
Truck
NULL
Load: 5 Tons
In the kingdom of DataLand, these terms help everyone stay organized and work together
harmoniously:
• Strong Entities are the independent heroes.
• Weak Entities are the sidekicks who need a hero.
• Relationships are the bonds that connect them.
• Specialization gives heroes unique roles.
• Generalization unites diverse heroes under one banner.
This story simplifies database management concepts and gives students a way to visualize
the terms!
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SECTION-B
3. How BCNF performs better than Third normal form? Explain using example.
Ans: The Kingdom of Tables
Once upon a time in the Kingdom of Tables, the wise King Normal ruled over the kingdom.
His goal was to make sure there was no confusion in the library (the database), where all the
royal information was stored. The King had created laws to keep everything in order.
Meet the Rules
1. 1st Normal Form (1NF): All books had to be neatly written in rows, one piece of
information per cell. No messy or multiple values in a single box!
2. 2nd Normal Form (2NF): The library separated important topics (attributes) into
smaller sections so that no part depended only on a part of a book's number
(primary key).
3. 3rd Normal Form (3NF): Each section was checked to ensure no extra, unnecessary
relationships. Everything directly related to the book stayed only in its own section.
A New Problem in the Kingdom
Even after following 3NF, some strange issues arose. For example, whenever a librarian
updated an important "secret code," they had to update it in too many places! Sometimes
they forgot, leading to confusion.
The King realized that even in 3NF, some "hidden powers" called dependencies were
messing things up. These dependencies were like invisible strings tying unrelated sections
together, creating chaos. So, the King called his wizard, Boyce-Codd, to solve this problem.
Boyce-Codd's Brilliant Solution (BCNF)
Boyce-Codd created a stricter law called BCNF (Boyce-Codd Normal Form). He declared:
"If a section of the library (table) has any hidden powers (dependencies), those powers must
belong only to the leader of that section (the primary key)!"
This meant that no sneaky relationships were allowed unless they directly followed the rules
of the section's primary key.
How BCNF is Better Than 3NF
Let’s look at a real-life example to understand how BCNF improved the library’s system.
Example: A Class Enrollment Table
Imagine the Kingdom of Tables has a school, and here’s how the student data is organized:
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Student
Course
Instructor
Alice
Math
Dr. Smith
Bob
Physics
Dr. Taylor
Alice
Physics
Dr. Taylor
• Primary Key: (Student, Course)
• Rule in 3NF: The table is free from unnecessary relationships, but there’s a
problem...
Hidden Dependency Problem:
• Each course has only one instructor. So, "Course" → "Instructor" is a hidden
dependency.
• If the course changes its instructor, we have to update this info everywhere in the
table. This leads to duplication and potential errors.
BCNF to the Rescue
In BCNF, Boyce-Codd said: "Separate the hidden dependency into its own section!"
Now the table is split into two smaller tables:
1. Student-Course Table:
Student
Course
Alice
Math
Bob
Physics
Alice
Physics
2. Course-Instructor Table:
Course
Instructor
Math
Dr. Smith
Physics
Dr. Taylor
Why is This Better?
• No Duplication: Each piece of information is stored only once.
• Easier Updates: If Dr. Taylor stops teaching Physics, we only need to update the
Course-Instructor table.
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• Less Error-Prone: No risk of forgetting to update a value in one place and causing
inconsistency.
The Moral of the Story
BCNF is like a super-strict librarian who says, "Every hidden rule must be directly related to
the book’s leader (primary key)." This extra layer of strictness helps keep the database
cleaner, avoids duplication, and makes updates much easier.
Summary of BCNF vs. 3NF
Feature
3NF
BCNF
Handles
Dependencies
Only removes non-key
dependencies
Removes all dependencies not tied to
the key
Risk of Duplication
Higher (in some cases)
Lower
Complexity
Easier to implement
Stricter rules
Best for...
General use cases
Complex cases with hidden
dependencies
BCNF’s Happy Ending
Thanks to Boyce-Codd's brilliance, the Kingdom of Tables became an efficient, well-
organized place. Librarians celebrated because updates became hassle-free, and no data
was ever duplicated or lost again!
This simple story teaches that BCNF performs better than 3NF in scenarios where hidden
dependencies exist, ensuring a cleaner and more reliable database structure.
4. What is Concurrency Control? What are its potential problems? Explain the concept of
Lock-based Protocols in CC.
Ans: The Concurrency Control Story: A Busy Pizza Shop
Imagine a busy pizza shop where customers keep coming in to order their favorite pizzas.
The shop has only one oven, and multiple chefs want to use it at the same time. The owner,
Mr. Data Base, realizes that without proper rules, the chefs might fight over the oven or
even ruin the pizzas. This is exactly what Concurrency Control (CC) does in the world of
databases. It ensures everyone gets their turn to use the oven without chaos.
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Now, let’s break it down step by step.
What is Concurrency Control?
Concurrency Control is like Mr. Data Base’s rulebook to ensure multiple chefs (users or
transactions) can work together without creating problems. It’s used in databases to make
sure multiple transactions happen smoothly without disturbing each other, even if they run
simultaneously.
For example:
• Chef Alice starts baking a pizza. She wants to use the oven for 10 minutes.
• Chef Bob comes in and tries to use the same oven during Alice’s time.
Without rules, Bob might interrupt Alice, and her pizza could burn or end up half-
baked!
What Problems Can Happen Without Concurrency Control?
1. Dirty Reads:
Imagine Alice started baking her pizza, and Bob pulls it out midway to bake his own.
Bob’s pizza will be fine, but Alice’s pizza is ruined, and she might serve something
incomplete to her customer.
2. Lost Updates:
Suppose Alice puts her pizza in the oven, sets the temperature, and walks away. Bob
comes in, changes the temperature for his pizza without knowing about Alice's
settings, ruining both pizzas.
3. Unrepeatable Reads:
Alice bakes her pizza and checks if it's done. While she's away, Bob adjusts the
toppings on Alice’s pizza. When Alice checks again, the toppings have changed
without her knowing.
4. Phantom Reads:
If Alice takes stock of the ingredients and plans her pizzas, but during this time, Bob
sneaks in and uses the cheese, Alice might find herself short of ingredients
unexpectedly.
The Solution: Lock-Based Protocols
To avoid these problems, Mr. Data Base comes up with Lock-Based Protocols. Locks are like
keys to the oven. If a chef locks the oven, no one else can use it until they’re done.
There are two main types of locks:
1. Shared Lock (S):
o Multiple chefs can peek into the oven without changing anything.
o Example: Alice and Bob can both check if the oven is free, but they can’t
adjust the temperature or put in their pizzas.
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2. Exclusive Lock (X):
o Only one chef can use the oven at a time.
o Example: If Alice locks the oven to bake her pizza, Bob must wait until she’s
done to use it.
How Lock-Based Protocols Work
Here’s how Mr. Data Base ensures fairness with these locks:
1. Two-Phase Locking Protocol (2PL):
o This rule has two phases:
a. Growing Phase: Chefs acquire all the locks they need. Once locked, others
must wait.
b. Shrinking Phase: Chefs release the locks once they’re done, making the
oven free for others.
Example:
o Alice locks the oven (growing phase).
o She bakes her pizza and then unlocks the oven (shrinking phase).
o Bob can now lock and use the oven.
2. Strict Two-Phase Locking (Strict 2PL):
o Chefs hold onto their locks until the very end, ensuring no mistakes happen.
o Example: Alice keeps the oven locked until her pizza is served.
3. Deadlocks:
o Sometimes, two chefs lock different things and wait for each other to unlock.
This causes a traffic jam.
o Example: Alice locks the oven, and Bob locks the fridge. They both wait for
each other to unlock their resource, and nothing gets done.
Solution to Deadlocks:
o Use a timeout: If a chef waits too long, they’re asked to step aside.
o Follow a priority system: Decide who gets the oven based on rules like “first
come, first served.”
Why is Concurrency Control Important?
1. Data Accuracy: Prevents the ruin of pizzas (data corruption).
2. Consistency: Ensures every pizza order is completed without mistakes.
3. Fairness: Everyone gets their turn with the oven.
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4. Safety: Prevents chefs from interfering with each other’s work.
Conclusion
Concurrency Control is like the secret sauce that keeps Mr. Data Base’s pizza shop running
smoothly. With lock-based protocols, chefs (transactions) can work together without
burning pizzas (data). Problems like dirty reads, lost updates, and deadlocks are avoided
with clear rules and communication.
Just remember, whether it’s baking pizzas or running a database, good management
ensures a happy and efficient kitchen—or system!
SECTION-C
5.(a) Explain the concept of DCL. Define DCL statements using suitable examples.
(b) Explain the concept of SQL Intersection using example.
Ans: Imagine you're the head of a big library. This library is not just any ordinary library—
it's a digital library, where thousands of books (data) are stored in a massive computer
system (database). Now, to keep things organized, we need to make sure that only certain
people can read, add, edit, or remove books from the library.
So, how do we control who gets to do what? Well, that's where DCL comes into the picture!
DCL stands for Data Control Language. Think of DCL as the "security guard" of the library,
making sure only the right people can access or modify the books (data) in the system.
What is DCL?
In the world of Database Management Systems (DBMS), DCL (Data Control Language) is a
set of commands used to control access to data in a database. These commands determine
who can see or change the data. It's like a digital bouncer at the door of a club (database),
letting the right people in and making sure no one does anything they’re not supposed to.
Why Do We Need DCL?
Let’s go back to the library analogy. You don't want just anyone walking into the library and
taking books off the shelves. Some people might be allowed to borrow books, others can
just read, and some should only be allowed to put books back in their correct place without
changing anything.
In the same way, in a database, we need to control who can do what—whether it's reading
the data, inserting new data, updating existing data, or deleting it. DCL provides the tools to
do this.
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The Two Main DCL Commands
DCL is mainly made up of two important commands:
1. GRANT
2. REVOKE
These commands are used to grant or revoke (remove) permissions from database users,
making sure they only have access to the data they need.
Let’s take a closer look at each of these commands.
1. GRANT: Giving Permissions
Imagine you're the head librarian, and you want to give someone special permission to
borrow books from the library. You hand them a special card that says, “This person is
allowed to borrow books whenever they want!” This is exactly what the GRANT command
does in the world of databases.
GRANT allows a database administrator (DBA) or the person in charge to give certain
privileges or permissions to a user, like allowing them to view, add, or change data.
Example of GRANT
Let’s say you have a students table, and you want to allow a specific user to select (view)
data from this table.
Here’s what the SQL command would look like:
GRANT SELECT ON students TO john_doe;
In this example:
• SELECT is the privilege (permission) being granted.
• students is the table the privilege is being granted for.
• john_doe is the user who is being given permission.
So, john_doe now has permission to view the data in the students table, but nothing more
(like changing or deleting anything).
Different Types of Permissions You Can Grant
With GRANT, you can give users a wide variety of permissions, like:
• SELECT: Allows the user to read data from the table.
• INSERT: Allows the user to add new data to the table.
• UPDATE: Allows the user to modify existing data in the table.
• DELETE: Allows the user to delete data from the table.
• ALL: Grants all the above permissions to the user.
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For example:
GRANT SELECT, INSERT, UPDATE ON students TO jane_smith;
In this case, jane_smith can read, add, and modify data in the students table.
2. REVOKE: Taking Away Permissions
What happens if someone abuses their privileges? Or if you simply want to take back the
permissions you gave them? That’s when the REVOKE command comes into play.
REVOKE is the opposite of GRANT. It’s used to remove the permissions that were previously
granted to a user. It’s like taking back the special library card you gave to someone who
wasn’t following the rules.
Example of REVOKE
If you want to take away john_doe's permission to view the data in the students table, you
would write the following:
REVOKE SELECT ON students FROM john_doe;
In this example:
• SELECT is the privilege that is being revoked.
• students is the table the permission is related to.
• john_doe is the user whose permission is being taken away.
Now, john_doe can no longer see the data in the students table.
Putting It All Together: The Power of DCL
Imagine you're working as a database administrator (DBA) at a big company. Your job is to
make sure that employees can do their work efficiently without accessing data they
shouldn’t. Here’s how you might use GRANT and REVOKE to keep things in order:
1. GRANT permissions to different employees based on their job role:
o HR Department can read and update employee records (SELECT, UPDATE).
o Finance Department can read employee salaries (SELECT).
o Managers can add new employees to the database (INSERT).
2. Later, you may want to REVOKE some permissions:
o If an employee leaves the company, you would REVOKE their permissions to
ensure they no longer have access to the database.
DCL in Action: Real-Life Scenario
Let’s make this more relatable with a story:
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Imagine you’re in charge of a cool, secret club where only certain members can access
exclusive data (like club finances, secret messages, etc.).
• You use GRANT to give specific members access to secret files or data—based on
their role in the club.
• But one day, you notice that a member, Tom, is sharing club secrets outside the
group. You decide to remove his access. So, you use REVOKE to take away Tom’s
access to the club’s data.
This process of granting and revoking access is exactly what happens in databases. It helps
keep sensitive information safe and makes sure that only authorized people can make
changes.
How Does DCL Fit into the Bigger Picture?
DCL is just one piece of the puzzle in the world of databases. But it's a very important one!
When building a secure and organized database, DCL ensures that data remains safe and
controlled.
Here’s how DCL interacts with other components in DBMS:
• DDL (Data Definition Language): This is used to define and manage the structure of
the database itself—like creating tables or changing their design. DCL comes in after
the structure is set, helping control access to the data.
• DML (Data Manipulation Language): This is used for interacting with the data
itself—like inserting, updating, and deleting data. But even before using DML
commands, you must ensure that the right people have the appropriate permissions
(using DCL).
Conclusion: Mastering DCL
In summary, DCL is like a powerful tool that helps you control access to data in a database.
By using GRANT and REVOKE, you can carefully manage who can view, edit, or delete data—
ensuring your database remains organized and secure.
Here’s a quick recap:
• GRANT: Give permissions to users (allow them to view, edit, etc. data).
• REVOKE: Take away those permissions when needed.
So, the next time you're managing a database, just think of yourself as the head librarian,
keeping the library safe and organized, making sure only the right people can access the
right books. And that’s the magic of DCL in action!
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6. What do you mean by Database Triggers? Discuss its different types. Write the syntax
for creating a database trigger using different parameters.
ANS: Imagine a magic book in a wizard's library . Every time someone writes
something in the book or erases something from it, the book reacts in a unique way: it could
sound an alarm , light up , or send a magical bird to deliver a message. These
magical reactions are like triggers in a database. A Database Trigger is a set of instructions
(like the magic spells) that automatically runs when something specific happens in the
database.
In simpler terms, triggers are actions in a database that happen automatically when you
make a change, such as adding, updating, or deleting data. It’s like a helper in the
background keeping things organized and consistent without you even noticing.
Types of Database Triggers
Think of triggers as magical assistants with different specialties. Here's how they work:
1. Before Trigger
• When it works: Before a change (like adding, updating, or deleting data) is applied to
the database.
• What it does: Checks or prepares something beforehand.
• Example: Imagine a grocery store . Before someone adds items to their cart, the
system checks if the item is in stock.
2. After Trigger
• When it works: After a change is made to the database.
• What it does: Takes action based on the completed change.
• Example: At the same grocery store, once a payment is processed, a receipt is
generated automatically.
3. Instead Of Trigger
• When it works: Instead of performing the normal action, it performs a custom
action.
• What it does: Overrides the usual operation with something special.
• Example: Think of a security guard . Instead of letting everyone enter a restricted
area, the guard verifies their ID first.
4. Row-Level Trigger
• When it works: Executes for each row affected by the operation.
• What it does: Monitors and reacts to changes in specific rows of the table.
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• Example: A garden where a gardener waters each plant individually when it’s
dry.
5. Statement-Level Trigger
• When it works: Executes once for the entire SQL statement, no matter how many
rows it affects.
• What it does: Focuses on the entire operation rather than individual rows.
• Example: A librarian who organizes an entire shelf instead of focusing on one
book.
How to Create a Trigger (The Magic Spell )
Creating a trigger is like writing a recipe for what should happen automatically. Here's
how you can do it, step-by-step, using SQL:
Basic Syntax:
CREATE OR REPLACE TRIGGER trigger_name
[BEFORE | AFTER | INSTEAD OF] [INSERT | UPDATE | DELETE]
ON table_name
[FOR EACH ROW]
DECLARE
-- Declare variables if needed
BEGIN
-- Write the actions to perform
END;
/
Parameters Explained:
1. Trigger Name: Like naming the spell, e.g., order_trigger.
2. BEFORE/AFTER/INSTEAD OF: Decide when the trigger should fire.
3. INSERT/UPDATE/DELETE: Specify what action will activate the trigger.
4. ON Table Name: The table where the trigger will be attached.
5. FOR EACH ROW: Whether the trigger works on each affected row or the entire
operation.
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Examples of Triggers
Example 1: Before Insert Trigger
Imagine a school where student roll numbers must always start with "S-". Here's how
you can create a trigger to ensure that:
CREATE OR REPLACE TRIGGER student_before_insert
BEFORE INSERT ON students
FOR EACH ROW
BEGIN
IF :NEW.roll_no NOT LIKE 'S-%' THEN
:NEW.roll_no := 'S-' || :NEW.roll_no;
END IF;
END;
/
• What it does: Before adding a new student, it checks if the roll number starts with
"S-". If not, it fixes it.
Example 2: After Update Trigger
In a bank , whenever a transaction occurs, you want to log it. Here's how:
CREATE OR REPLACE TRIGGER transaction_log
AFTER UPDATE ON accounts
FOR EACH ROW
BEGIN
INSERT INTO logs (account_id, old_balance, new_balance, updated_at)
VALUES (:OLD.id, :OLD.balance, :NEW.balance, SYSDATE);
END;
/
• What it does: After updating an account, it logs the old and new balances.
Example 3: Instead Of Trigger
Let’s say a real-estate company has a view combining data from multiple tables. You can
use a trigger to handle insert operations for this view:
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CREATE OR REPLACE TRIGGER handle_view_insert
INSTEAD OF INSERT ON property_view
FOR EACH ROW
BEGIN
INSERT INTO properties (id, name, price)
VALUES (:NEW.id, :NEW.name, :NEW.price);
END;
/
• What it does: Instead of trying to insert into the view, it directs the data to the
correct table.
Why Are Triggers Important?
Triggers are like unsung heroes. Here’s why they matter:
1. Automation: They save you from manually writing repetitive tasks.
2. Consistency: Ensure rules and constraints are always followed.
3. Logging: Keep track of changes automatically.
4. Custom Actions: Enable custom workflows specific to your application.
Key Things to Remember
1. Triggers are powerful but should be used wisely.
o Overusing triggers can make the database slower and harder to debug.
2. They’re automatic but require careful planning.
o Test your triggers thoroughly to avoid unexpected issues.
3. Security is important.
o Triggers can access sensitive data, so make sure they’re secure.
Fun Recap
• Trigger: A magic spell in the database.
• Before Trigger: Prepares for a change.
• After Trigger: Acts after a change.
• Instead Of Trigger: Does something custom.
• Row-Level vs. Statement-Level: Focus on rows vs. entire actions.
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Triggers are like your secret helpers, ensuring everything runs smoothly in the magical world
of databases. When used correctly, they make life easier for database wizards (like you!).
SECTION-D
7. How Big Data is used to solve the problems of large database? Explain its use in Data
Analytics.
Ans: Imagine you have a gigantic mountain of books that keeps growing every second, and
you need to find specific books quickly or use them to solve mysteries about the world. How
does Big Data act like a magical librarian to manage this mountain and help solve these
mysteries using Data Analytics?
The Problem: Too Much Data, Too Little Time
In today’s world, every click, swipe, or even a step taken while wearing a fitness tracker adds
to a growing pile of information. This pile, known as Big Data, is way too huge for traditional
databases to handle. Traditional databases, like filing cabinets, are great for storing and
organizing smaller piles of data but struggle when the data is growing super-fast and coming
from everywhere: phones, satellites, websites, sensors, and more.
Here’s where Big Data comes in. It’s like hiring a super-smart librarian who can manage this
ever-growing mountain of books (data) while working lightning-fast to find patterns,
insights, or answers.
What Makes Big Data Special?
Big Data has three magical powers, called the Three Vs:
1. Volume – It can handle data that’s unimaginably huge. Think billions of tweets,
weather records, or online purchases every day.
2. Velocity – It works super quickly, processing data as it’s created. For example,
analyzing live sports data or monitoring stock market trends in real time.
3. Variety – It’s versatile and can work with many types of data: texts, photos, videos,
GPS locations, and more.
How Big Data Fixes the Problem of Large Databases
Traditional databases store data neatly, like books in a library. But if 10,000 people walk into
the library and throw books on the floor every second, the librarian wouldn’t know where to
start. Big Data tools, such as Hadoop and Spark, are like magical assistants that:
1. Break the mountain into smaller chunks and handle each part separately (parallel
processing).
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2. Find connections between different types of data, like matching a picture of a recipe
with the ingredients someone recently bought.
3. Keep learning from past mistakes, getting better over time through something called
Machine Learning.
How Big Data Works in Data Analytics
Now let’s talk about the magical powers Big Data brings to Data Analytics. Think of Data
Analytics as a detective who solves puzzles using clues hidden in data. Big Data helps this
detective work faster and smarter.
1. Spotting Patterns
Big Data can sift through millions of data points to find patterns that humans might miss.
• Example: Streaming services like Netflix analyze your watching habits to recommend
shows you’ll love. They track patterns in what millions of people like to watch.
2. Making Predictions
Big Data predicts what might happen next by learning from past data.
• Example: Online stores like Amazon use Big Data to guess what you might want to
buy based on your browsing history.
3. Solving Real-Life Problems
Big Data analytics is used to tackle some of the biggest challenges in the world:
• Healthcare: Doctors use Big Data to predict disease outbreaks or find personalized
treatments for patients.
• Environment: Scientists use it to monitor climate change or track endangered
animals.
• Traffic: Cities use Big Data to reduce traffic jams by analyzing real-time data from
GPS and sensors.
How Big Data and Analytics Work Together
Let’s return to our story of the magical librarian and the detective. The librarian (Big Data)
collects, organizes, and manages the giant pile of books, while the detective (Data Analytics)
uses these books to solve mysteries. Here’s how they team up:
1. Collect Data – Big Data tools gather data from everywhere, whether it’s social
media, sensors, or bank transactions.
2. Process Data – The tools clean and organize the data so it’s ready for analysis.
3. Analyze Data – Using analytics techniques like Data Mining (digging for useful
information), Statistical Analysis, and AI algorithms, the detective finds insights.
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4. Visualize Insights – Analytics creates charts and graphs to explain the findings,
making it easy for people to understand.
Examples of Big Data in Action
In Business
Imagine a supermarket chain wants to increase sales. Using Big Data, they analyze millions
of receipts to find out which products are bought together. They discover that customers
often buy bread and bananas together on rainy days. They use this insight to place these
items next to each other and offer discounts, increasing sales.
In Sports
Big Data helps teams track players’ performances and predict injuries. It even helps coaches
plan strategies by analyzing the opponent’s gameplay.
In Social Media
Platforms like Facebook and Instagram analyze Big Data to show users content they’ll find
most engaging, keeping them hooked.
In Disaster Management
Big Data predicts natural disasters by analyzing weather data. This helps governments
prepare and save lives.
How It All Comes Together: A Fun Analogy
Let’s say you’re running a pizza delivery business.
1. The Data: Customer orders, delivery times, ingredients inventory, online reviews.
2. The Problem: You want to deliver faster, reduce waste, and keep customers happy.
3. The Big Data Solution:
o Collect: Gather data from apps, GPS, and reviews.
o Process: Use Big Data tools to clean and sort this information.
o Analyze: Find patterns, like which pizza is most popular on weekends or
which areas take the longest to deliver to.
o Act: Use the insights to stock up on popular ingredients, hire more drivers in
busy areas, and offer discounts on slow-moving items.
The result? Happier customers, more efficient deliveries, and higher profits!
Conclusion: Why Big Data Matters
Big Data isn’t just a tool; it’s a game-changer. It helps organizations make smarter decisions,
solve big problems, and create amazing experiences. From helping doctors save lives to
making your Netflix binge-watching more fun, Big Data and Analytics are shaping the future.
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Just remember:
• Big Data is the librarian that handles massive amounts of information.
• Data Analytics is the detective that solves mysteries using that information.
8. Differentiate between SQL and NoSQL. Which one is better to use ?Explain using examples.
Ans: The Tale of Two Data Friends: SQL and NoSQL
Once upon a time, in the land of Data Kingdom, there lived two best friends: SQL
(Structured Query Language) and NoSQL (Not Only SQL). Both were hardworking, talented,
and loved helping people organize their data. However, they had very different ways of
doing things. Let’s learn about their unique personalities and figure out when each one is
the better choice for our projects.
SQL: The Organized Librarian
SQL is like a strict, super-organized librarian who loves rules. She arranges all the books
(data) in neat rows and columns, like shelves in a library. Every book belongs to a category,
and you can easily find it because she uses a detailed catalog system.
• Key Features of SQL:
1. Tables and Rows: SQL uses tables to store data in a structured way, just like
spreadsheets.
2. Schema: Every table follows strict rules, like a table of student data having
fixed columns: Student ID, Name, and Grade.
3. ACID Compliance: SQL ensures data is always accurate and reliable. Think of
it as double-checking that every borrowed book is returned before lending it
out again.
4. Language: SQL uses commands like SELECT, INSERT, and UPDATE to
communicate with databases.
• Examples of SQL Databases:
o MySQL
o PostgreSQL
o SQLite
o Oracle Database
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NoSQL: The Flexible Artist
On the other hand, NoSQL is like an artist who loves creativity and freedom. He doesn’t
bother with strict rules and lets you store data however you like—pictures, documents, or
even messy drawings.
• Key Features of NoSQL:
1. Flexible Structure: NoSQL doesn’t need tables. It can store data in formats
like documents, key-value pairs, or graphs.
2. No Schema Needed: There are no fixed rules. You can add new information
anytime without worrying about breaking the structure.
3. Scalability: NoSQL can easily grow and handle large amounts of data, like an
expandable storage box.
4. Speed: It’s faster for certain tasks, especially when handling unstructured or
semi-structured data.
• Examples of NoSQL Databases:
o MongoDB
o Cassandra
o Redis
o Couchbase
A Friendly Chat: SQL vs. NoSQL
One day, SQL and NoSQL met for a chat to discuss their differences and strengths.
SQL:
"I’m best for tasks that need strict rules and organized data. If you’re running a bank or an e-
commerce site, you can trust me to keep your records safe and consistent."
NoSQL:
"That’s great, SQL! But I’m perfect for handling messy or unstructured data, like social
media profiles or game stats. I’m fast, flexible, and can handle big changes easily."
SQL vs. NoSQL: The Comparison Table
Feature
SQL
NoSQL
Structure
Relational (tables, rows, columns)
Flexible (documents, key-value,
graph)
Schema
Fixed schema
Dynamic schema
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Feature
SQL
NoSQL
Scalability
Vertical (add more resources to one
server)
Horizontal (add more servers)
Data Type
Structured
Unstructured or semi-structured
Best For
Banking, payroll, and ERP systems
Social media, IoT, and big data
ACID
Compliance
Fully compliant
Eventual consistency in most
cases
Examples
MySQL, PostgreSQL
MongoDB, Cassandra
Which One is Better?
The answer depends on the story you’re working on!
1. Choose SQL if:
o You have structured data with clear relationships (e.g., student records or
employee databases).
o You need strict data integrity (e.g., financial systems).
Example:
Imagine running a school database. You’d use SQL to ensure every student has a unique ID,
name, and grades stored neatly in a table.
CREATE TABLE Students (
StudentID INT PRIMARY KEY,
Name VARCHAR(100),
Grade CHAR(1)
Here, SQL ensures that no two students have the same ID and that data stays consistent.
2. Choose NoSQL if:
o You’re working with large-scale, unstructured data (e.g., storing user activity
from a social media app).
o You need flexibility to adapt quickly to changes.
Example:
For a social media app, you might use a NoSQL database like MongoDB to store user profiles
in a document format:
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json
This format is easy to update if a user adds new information, like a profile picture.
A Real-Life Example: Choosing the Right Friend
Let’s say you’re building an online bookstore.
• SQL’s Role:
You’d use SQL to handle the customer database, where details like names,
addresses, and payment information need to be accurate and secure.
• NoSQL’s Role:
To store customer reviews, you might use NoSQL, since reviews can vary widely in
length and format.
Conclusion: Friends for Different Needs
SQL and NoSQL aren’t enemies—they’re just suited for different kinds of jobs. If your story
involves organized data and strong rules, SQL is your go-to. But if you’re dealing with
creative, ever-changing data, NoSQL is your hero.
Remember, in the land of Data Kingdom, both SQL and NoSQL are valuable friends. Choose
the one that fits your adventure, and your data will live happily ever after!
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