SQL Programming Resources

🔥 Now, Let’s move to the next topic:

✅ UNION & UNION ALL in SQL

🧠 1. What is UNION?
UNION is used to combine results from multiple SELECT queries.

"Merge data from two tables into one result.”

⚡ 2. Rules for UNION
✔ Same number of columns
✔ Same datatype/order of columns

📊 Example Tables

👨‍💼 employees₂024
name
• Amit
• Neha

👨‍💼 employees₂025
name
• Ravi
• Neha

🔥 3. UNION Example

SELECT name FROM employees_2024
UNION
SELECT name FROM employees_2025;

✔ Removes duplicates automatically

✅ Result
name
• Amit
• Neha
• Ravi

⚡ 4. UNION ALL

SELECT name FROM employees_2024
UNION ALL
SELECT name FROM employees_2025;

✔ Keeps duplicates
✔ Faster than UNION

✅ Result
name
• Amit
• Neha
• Ravi
• Neha

🔥 5. UNION vs UNION ALL

UNION
• Removes duplicates
• Slower
• Doesn't keep all rows

UNION ALL
• Doesn't remove duplicates
• Faster
• Keeps all rows

⚡ 6. ORDER BY with UNION

SELECT name FROM employees_2024
UNION
SELECT name FROM employees_2025
ORDER BY name;

🎯 7. Practice Tasks
1. Combine employee names using UNION
2. Combine employee names using UNION ALL
3. Identify duplicate removal
4. Sort UNION result using ORDER BY
5. Compare UNION vs UNION ALL output

⚡ Mini Challenge 🔥
👉 Combine customer names from two branches and keep duplicates

🔥 Mini Challenge Solution 💯

👉 Since duplicates should remain → use UNION ALL

✅ Example Tables

🏢 branch_a_customers
customer_name
• Amit
• Neha

🏢 branch_b_customers
customer_name
• Ravi
• Neha

✅ SQL Solution

SELECT customer_name
FROM branch_a_customers

UNION ALL

SELECT customer_name
FROM branch_b_customers;

✅ Result
customer_name
• Amit
• Neha
• Ravi
• Neha

✔ Duplicate Neha is preserved 💯

🧠 Why UNION ALL?
👉 UNION → removes duplicates
👉 UNION ALL → keeps duplicates + faster

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🔥 Now, let's move to the next topic:

✅ SQL Constraints

Essential for data integrity & important in interviews 💯

🧠 1. What are Constraints in SQL?
Constraints are rules applied on table columns
👉 to maintain accurate & valid data

Think like this 👇
👉 “Database safety rules”

⚡ 2. Why Use Constraints?
✔ Prevent invalid data
✔ Maintain consistency
✔ Improve data integrity
✔ Enforce relationships

📊 Types of Constraints

NOT NULL
• Purpose: Prevent NULL values

UNIQUE
• Purpose: No duplicate values

PRIMARY KEY
• Purpose: Unique identifier

FOREIGN KEY
• Purpose: Create relationship

CHECK
• Purpose: Apply condition

DEFAULT
• Purpose: Set default value

🔥 3. NOT NULL Constraint
👉 Column cannot contain NULL

CREATE TABLE employees (
    emp_id INT,
    name VARCHAR(50) NOT NULL
);

🔥 4. UNIQUE Constraint
👉 Prevent duplicate values

CREATE TABLE users (
    email VARCHAR(100) UNIQUE
);

🔥 5. PRIMARY KEY
👉 Unique + NOT NULL

CREATE TABLE employees (
    emp_id INT PRIMARY KEY,
    name VARCHAR(50)
);

✔ Every row must have unique emp_id

🔥 6. FOREIGN KEY
👉 Creates relationship between tables

CREATE TABLE employees (
    emp_id INT PRIMARY KEY,
    dept_id INT,
    FOREIGN KEY (dept_id)
    REFERENCES departments(dept_id)
);

✔ dept_id must exist in departments table

🔥 7. CHECK Constraint
👉 Restrict values using condition

CREATE TABLE employees (
    salary INT CHECK (salary > 0)
);

✔ Salary cannot be negative

🔥 8. DEFAULT Constraint
👉 Assign default value automatically

CREATE TABLE employees (
    city VARCHAR(50) DEFAULT 'Pune'
);

🎯 9. Practice Tasks
1. Create table using PRIMARY KEY
2. Add UNIQUE constraint on email
3. Create FOREIGN KEY relationship
4. Use CHECK for salary > 0
5. Add DEFAULT city value

⚡ Mini Challenge 🔥
👉 Create students table with:
• student_id → PRIMARY KEY
• email → UNIQUE
• age > 18 using CHECK
• city default = 'Mumbai'

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🔥 Now, let’s move to the next topic:

✅ Denormalization in SQL

🧠 1. What is Denormalization?
Denormalization means
👉 combining normalized tables
👉 to improve query performance

Think like this 👇
✅ Normalization → reduce redundancy
✅ Denormalization → improve speed

⚡ 2. Why Use Denormalization?
✔ Faster queries
✔ Fewer JOIN operations
✔ Better reporting performance

❌ But:
- Data redundancy increases
- Updates become harder

📊 Example (Normalized Structure)

👨‍🎓 Students
student_id: 1
name: Amit

📘 Courses
course_id: 101
course: SQL

📝 Enrollment
student_id: 1
course_id: 101

👉 Need JOINs to get full info

⚡ Denormalized Structure
student_id: 1
name: Amit
course: SQL

✔ Faster retrieval
❌ Duplicate data possible

🔥 3. Normalization vs Denormalization

Feature: Redundancy → Normalization: Low → Denormalization: High

Feature: Query Speed → Normalization: Slower → Denormalization: Faster

Feature: Storage → Normalization: Less → Denormalization: More

Feature: JOINs → Normalization: More → Denormalization: Fewer

⚡ 4. Real-World Usage
✅ Normalization Used In:
- Banking systems
- Transaction systems
- OLTP databases

✅ Denormalization Used In:
- Reporting systems
- Dashboards
- Data warehouses

🎯 5. Example Query

👉 Normalized (requires JOIN)

SELECT s.name, c.course
FROM students s
JOIN enrollment e
ON s.student_id = e.student_id
JOIN courses c
ON e.course_id = c.course_id;

👉 Denormalized

SELECT name, course
FROM student_courses;

✔ Simpler & faster

🎯 6. Practice Tasks
1. Identify normalized tables
2. Create denormalized version
3. Compare JOIN vs direct query
4. Find redundancy in denormalized table
5. Decide when denormalization is useful

⚡ Mini Challenge 🔥
👉 Design a denormalized sales report table for faster dashboard queries

✅ Pro Tips:
👉 “Normalization improves consistency”
👉 “Denormalization improves performance”

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