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SQL Programming Resources
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Find top SQL resources from global universities, cool projects, and learning materials for data analytics. Admin: @coderfun Useful links: heylink.me/DataAnalytics Promotions: @love_data
Показати більше📈 Аналітичний огляд Telegram-каналу SQL Programming Resources
Канал SQL Programming Resources (@sqlanalyst) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 75 774 підписників, посідаючи 1 696 місце в категорії Технології та додатки та 4 385 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 75 774 підписників.
За останніми даними від 04 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 472, а за останні 24 години на 62, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 3.14%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.31% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 2 376 переглядів. Протягом першої доби публікація в середньому набирає 992 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 3.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як row, sql, customer_id, logic, desc.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Find top SQL resources from global universities, cool projects, and learning materials for data analytics.
Admin: @coderfun
Useful links: heylink.me/DataAnalytics
Promotions: @love_data”
Завдяки високій частоті оновлень (останні дані отримано 05 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.
75 774
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+1467 днів
+47230 день
Архів дописів
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✅ SQL String Functions
🧠 1. What are String Functions?
String functions are used to
👉 manipulate text data
👉 clean messy data
👉 format outputs
Used heavily in:
✔ Data Analytics
✔ Reporting
✔ ETL processes
⚡ 2. Common String Functions
Function : Purpose
UPPER() : Convert to uppercase
LOWER() : Convert to lowercase
LENGTH() : Count characters
CONCAT() : Join strings
SUBSTRING() : Extract part of string
TRIM() : Remove spaces
REPLACE() : Replace text
🔥 3. UPPER() & LOWER()
SELECT UPPER(name) AS upper_name
FROM employees;
SELECT LOWER(name) AS lower_name
FROM employees;
🔥 4. LENGTH()
👉 Count number of characters
SELECT name, LENGTH(name) AS total_chars
FROM employees;
🔥 5. CONCAT()
👉 Combine strings
SELECT CONCAT(first_name, ' ', last_name) AS full_name
FROM employees;
🔥 6. SUBSTRING()
👉 Extract part of string
SELECT SUBSTRING(name, 1, 3)
FROM employees;
✔ Extracts first 3 characters
🔥 7. TRIM()
👉 Remove extra spaces
SELECT TRIM(' SQL ');
✔ Result → SQL
🔥 8. REPLACE()
👉 Replace text inside string
SELECT REPLACE('I love Java', 'Java', 'SQL');
✔ Result → I love SQL
🎯 9. Practice Tasks
1. Convert names to uppercase
2. Convert emails to lowercase
3. Combine first & last names
4. Extract first 4 letters of names
5. Remove extra spaces from city names
⚡ Mini Challenge 🔥
👉 Create employee usernames using:
first 3 letters of name + employee ID
Example:
Amit + 101 → Ami101
🔥 Mini Challenge Solution 💯
👉 Requirement:
Create username using:
• First 3 letters of name
• Employee ID
Example:
Amit + 101 → Ami101
✅ SQL Solution
SELECT name,
emp_id,
CONCAT(SUBSTRING(name, 1, 3), emp_id) AS username
FROM employees;
✅ Example Output
name : emp_id : username
Amit : 101 : Ami101
Neha : 102 : Neh102
Ravi : 103 : Rav103
🧠 How It Works
👉 SUBSTRING(name, 1, 3)
Extracts first 3 letters
👉 CONCAT()
Combines extracted text with employee ID
🔥 Real-World Usage:
String functions are commonly used for:
👉 Username generation
👉 Email formatting
👉 Data cleaning
👉 Customer IDs 💯
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✅ 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;
SELECT name FROM employees_2024
UNION ALL
SELECT name FROM employees_2025;
SELECT name FROM employees_2024
UNION
SELECT name FROM employees_2025
ORDER BY name;
SELECT customer_name
FROM branch_a_customers
UNION ALL
SELECT customer_name
FROM branch_b_customers;
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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
);
CREATE TABLE users (
email VARCHAR(100) UNIQUE
);
CREATE TABLE employees (
emp_id INT PRIMARY KEY,
name VARCHAR(50)
);
CREATE TABLE employees (
emp_id INT PRIMARY KEY,
dept_id INT,
FOREIGN KEY (dept_id)
REFERENCES departments(dept_id)
);
CREATE TABLE employees (
salary INT CHECK (salary > 0)
);
CREATE TABLE employees (
city VARCHAR(50) DEFAULT 'Pune'
);
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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;
SELECT name, course
FROM student_courses;
