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🔥 Now, let’s move to the next topic:

Triggers in SQL
(Automation inside database 💯)

🧠 1. What is a Trigger?
A Trigger is a special SQL block
👉 that runs automatically
👉 when an event happens in a table

Think like this 👇
👉 “Automatic action on INSERT / UPDATE / DELETE”

⚡ 2. Why Use Triggers?
✔ Automatic logging
✔ Data validation
✔ Audit tracking
✔ Prevent invalid operations

⚡ 3. Types of Triggers
BEFORE INSERT → Runs before inserting data
AFTER INSERT → Runs after inserting data
BEFORE UPDATE → Runs before updating
AFTER UPDATE → Runs after updating
BEFORE DELETE → Runs before deleting
AFTER DELETE → Runs after deleting

🔥 4. Basic Trigger Example
👉 Automatically log inserted employee

CREATE TABLE employee_log (
log_message VARCHAR(255)
);

DELIMITER //

CREATE TRIGGER after_employee_insert
AFTER INSERT ON employees
FOR EACH ROW
BEGIN
INSERT INTO employee_log
VALUES (CONCAT('New employee added: ', NEW.name));
END //

DELIMITER ;

🧠 5. Important Keywords
NEW → New inserted/updated value
OLD → Previous value before update/delete

⚡ 6. BEFORE UPDATE Example
👉 Prevent negative salary
DELIMITER //

CREATE TRIGGER check_salary
BEFORE UPDATE ON employees
FOR EACH ROW
BEGIN
IF NEW.salary < 0 THEN
SIGNAL SQLSTATE '45000'
SET MESSAGE_TEXT = 'Salary cannot be negative';
END IF;
END //

DELIMITER ;

❌ 7. Drop Trigger
DROP TRIGGER after_employee_insert;

🎯 8. Practice Tasks
1. Create AFTER INSERT trigger
2. Create BEFORE UPDATE trigger
3. Prevent negative salary using trigger
4. Log deleted employees
5. Drop created trigger

⚡ Mini Challenge 🔥
👉 Create trigger to automatically save deleted employee names into another table

🔥 Mini Challenge Solution

👉 Automatically save deleted employee names into another table

✅ Step 1: Create Log Table

CREATE TABLE deleted_employees (
emp_id INT,
name VARCHAR(50),
deleted_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

✅ Step 2: Create Trigger

DELIMITER //

CREATE TRIGGER log_deleted_employee
AFTER DELETE ON employees
FOR EACH ROW
BEGIN
INSERT INTO deleted_employees(emp_id, name)
VALUES (OLD.emp_id, OLD.name);
END //

DELIMITER ;

🧠 How It Works

👉 AFTER DELETE → runs automatically after deletion

👉 OLD.emp_id and OLD.name
Access deleted row values before they disappear

✅ Example

DELETE FROM employees
WHERE emp_id = 101;

✔ Deleted employee info automatically saved in deleted_employees table 💯

🔥 Pro Tip
Triggers are powerful but:
❌ Too many triggers can slow database
✅ Use them carefully 💯

Double Tap ❤️ For More

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Tools: Microsoft Excel, Google Sheets 
Mini Project: Analyze sales or survey data with PivotTables

🗓️ Week 2: SQL Fundamentals 
Goal: Learn to query databases efficiently 
Topics: SELECT, WHERE, JOIN, GROUP BY, subqueries 
Tools: MySQL, PostgreSQL, SQLite 
Mini Project: Query sample customer or sales database

🗓️ Week 3: Data Visualization Basics 
Goal: Create meaningful charts and graphs 
Topics: Bar charts, line charts, scatter plots, dashboards 
Tools: Tableau, Power BI, Excel charts 
Mini Project: Build dashboard to analyze sales trends

🗓️ Week 4: Data Cleaning & Preparation 
Goal: Handle messy data for analysis 
Topics: Handling missing values, duplicates, data types 
Tools: Excel, Python (Pandas) basics 
Mini Project: Clean and prepare real-world dataset for analysis

🗓️ Week 5: Statistics for Data Analysis 
Goal: Understand key statistical concepts 
Topics: Descriptive stats, distributions, correlation, hypothesis testing 
Tools: Excel, Python (SciPy, NumPy) 
Mini Project: Analyze survey data & draw insights

🗓️ Week 6: Advanced SQL & Database Concepts 
Goal: Optimize queries & explore database design basics 
Topics: Window functions, indexes, normalization 
Tools: SQL Server, MySQL 
Mini Project: Complex query for sales and customer analysis

🗓️ Week 7: Automating Analysis with Python 
Goal: Use Python for repetitive data tasks 
Topics: Pandas automation, data aggregation, visualization scripting 
Tools: Jupyter Notebook, Pandas, Matplotlib 
Mini Project: Automate monthly sales report generation

🗓️ Week 8: Capstone Project + Reporting 
Goal: End-to-end analysis and presentation 
Project Ideas: Customer segmentation, sales forecasting, churn analysis 
Tools: Tableau/Power BI for visualization + Python/SQL for backend 
Bonus: Present findings in a polished report or dashboard

💡 Tips:
⦁  Practice querying and analysis on public datasets (Kaggle, data.gov)
⦁  Join data challenges and community projects

💬 Tap ❤️ for the detailed explanation of each topic!

🔥 Now, let’s move to the next topic:

✅ Normalization in SQL

🧠 1. What is Normalization?

Normalization is the process of
👉 organizing data properly
👉 reducing duplicate data
👉 improving database structure

Think like this 👇
❌ Bad database → repeated data everywhere
✅ Normalized database → clean & efficient

⚡ 2. Why Normalization?
✔ Reduce data redundancy
✔ Avoid duplicate data
✔ Improve consistency
✔ Easier updates

📊 Example (Without Normalization)
Student data repeated multiple times
student_id student_name course instructor
1 Amit SQL Rahul
1 Amit Python Rahul

✅ After Normalization
👨‍🎓 Students Table
student_id student_name
1 Amit

📘 Courses Table
course_id course
101 SQL

📝 Enrollment Table
student_id course_id
1 101

✔ Cleaner structure 💯

🔥 3. Types of Normalization

Normal Form Purpose
1NF Remove repeating groups
2NF Remove partial dependency
3NF Remove transitive dependency

⚡ 4. First Normal Form (1NF)
👉 Each column should contain atomic values

❌ Wrong:
student courses
Amit SQL, Python

✅ Correct:
student course
Amit SQL
Amit Python

⚡ 5. Second Normal Form (2NF)
👉 Must already be in 1NF
👉 Remove partial dependency

Non-key columns should depend on full primary key

⚡ 6. Third Normal Form (3NF)
👉 Must already be in 2NF
👉 Remove transitive dependency

Non-key columns should depend ONLY on primary key

🎯 7. Practice Tasks
1. Identify duplicate data
2. Convert table into 1NF
3. Split data into multiple tables
4. Identify primary keys
5. Convert table into 3NF

⚡ Mini Challenge 🔥
👉 Normalize a student-course database into 3NF

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