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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
Больше📈 Аналитический обзор Telegram-канала SQL Programming Resources
Канал SQL Programming Resources (@sqlanalyst) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 75 774 подписчиков, занимая 1 696 место в категории Технологии и приложения и 4 385 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 75 774 подписчиков.
Согласно последним данным от 04 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 472, а за последние 24 часа — 62, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 3.14%. В первые 24 часа после публикации контент обычно набирает 1.31% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 376 просмотров. В течение первых суток публикация набирает 992 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 3.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как row, sql, customer_id, logic, desc.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“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”
Благодаря высокой частоте обновлений (последние данные получены 05 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
75 774
Подписчики
+6224 часа
+1467 дней
+47230 день
Архив постов
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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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🔥 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;
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;
