Data Analytics

🔄 Window Functions (ROW_NUMBER, RANK, PARTITION BY) Window functions perform calculations across rows related to the current row — but unlike GROUP BY, they don’t collapse your data! They are super useful for running totals, rankings, and finding duplicates. 1. ROW_NUMBER() Gives a unique number to each row within a partition of a result set. SELECT name, department, salary, ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS row_num FROM employees; Ranks employees by salary within each department. 2. RANK() vs DENSE_RANK() RANK() leaves gaps after ties. DENSE_RANK() doesn’t. SELECT name, salary, RANK() OVER (ORDER BY salary DESC) AS rank, DENSE_RANK() OVER (ORDER BY salary DESC) AS dense_rank FROM employees; 3. PARTITION BY It’s like a GROUP BY, but for window functions. SELECT department, name, salary, AVG(salary) OVER (PARTITION BY department) AS dept_avg_salary FROM employees; This shows each employee's salary alongside the average salary of their department — without collapsing the rows. Other Useful Window Functions: NTILE(n) – Divides rows into n buckets LAG() / LEAD() – Look at previous/next row’s value SUM() / AVG() over a window – for running totals React with ❤️ if you're pumped for the next one: ⚙️ Data Manipulation (INSERT, UPDATE, DELETE). Share with credits: https://t.me/sqlspecialist Hope it helps :)

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Here comes one of the cleanest and most powerful features in SQL: 🧠 Common Table Expressions (CTEs) A CTE (Common Table Expression) is a temporary result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. It makes your queries more readable and modular, especially when dealing with complex logic. Syntax: WITH cte_name AS ( SELECT column1, column2 FROM table_name WHERE condition ) SELECT * FROM cte_name; Example: Let’s say we want to get the top 3 highest-paid employees from each department. WITH ranked_employees AS ( SELECT name, department, salary, ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rank FROM employees ) SELECT * FROM ranked_employees WHERE rank <= 3; Why to use CTEs: - Easier to break down complex queries - You can use them multiple times in the main query - Readable and cleaner than subqueries You can chain multiple CTEs together and even write recursive CTEs for hierarchical data. React with ❤️ if you're excited for the next one: 🔄 Window Functions (ROW_NUMBER, RANK, PARTITION BY). Share with credits: https://t.me/sqlspecialist Hope it helps :)

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Now, let’s dive into a couple of powerful but often overlooked SQL features: 🧾 Views & Indexes (Basics) 1. Views A View is a virtual table based on a SQL query. It doesn’t store data itself — it just stores the query logic. Why use Views? - Simplifies complex queries - Improves code reusability - Adds a layer of security by hiding certain columns Creating a View: CREATE VIEW high_salary_employees AS SELECT name, salary FROM employees WHERE salary > 80000; Using the View: SELECT * FROM high_salary_employees; Updating a View: CREATE OR REPLACE VIEW high_salary_employees AS SELECT name, salary, department FROM employees WHERE salary > 80000; 2. Indexes An Index is like a book’s table of contents — it helps the database find data faster, especially in large tables. Why use Indexes? - Speeds up SELECT queries - Great for columns used in WHERE, JOIN, and ORDER BY Creating an Index: CREATE INDEX idx_employee_name ON employees(name); Indexes can slow down INSERT, UPDATE, DELETE because they need to update the index too. Don’t overuse them — only on frequently searched or joined columns. React with ❤️ if you’re ready for the next interesting concept: 🧠 Common Table Expressions (CTEs).

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🏷 Aliases & CASE Statements 1. Aliases (AS keyword) Aliases let you rename columns or tables temporarily to make your output cleaner or more readable. Column Alias Example: SELECT first_name AS name, salary AS monthly_income FROM employees; You’ll see name and monthly_income as column headers instead of raw column names. Table Alias Example: SELECT e.name, d.name FROM employees AS e JOIN departments AS d ON e.department_id = d.id; Here e and d are shortcuts for table names, making complex queries more readable. 2. CASE Statements CASE is SQL’s way of doing if-else logic inside queries. Example 1: Categorizing Data SELECT name, CASE WHEN salary > 80000 THEN 'High' WHEN salary BETWEEN 50000 AND 80000 THEN 'Medium' ELSE 'Low' END AS salary_category FROM employees; This assigns each employee a salary category. Example 2: Conditional Aggregation SELECT department, COUNT(CASE WHEN gender = 'Male' THEN 1 END) AS male_count, COUNT(CASE WHEN gender = 'Female' THEN 1 END) AS female_count FROM employees GROUP BY department; Counts males and females per department. Use aliases to simplify your SQL, and CASE when you need decision-making logic in queries. React with ❤️ if you're excited for the next topic :🧾 Views & Indexes Share with credits: https://t.me/sqlspecialist Hope it helps :)

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📦 Subqueries & Nested Queries A subquery is a query inside another query. You can use it in SELECT, FROM, or WHERE clauses to solve complex problems step-by-step. 1. Subquery in WHERE Clause Use this when you need to filter results based on another query. SELECT name FROM employees WHERE department_id = ( SELECT id FROM departments WHERE name = 'Sales' ); This finds all employees who work in the Sales department. 2. Subquery in SELECT Clause This lets you fetch calculated or related values for each row. SELECT name, (SELECT AVG(salary) FROM employees) AS avg_salary FROM employees; Shows each employee’s name along with the company’s average salary. 3. Subquery in FROM Clause (Inline View) Used when you want to treat the subquery like a temporary table. SELECT department, total FROM ( SELECT department, SUM(salary) AS total FROM employees GROUP BY department ) AS dept_summary; This groups salaries by department in a subquery, then fetches from it. Important: - Always alias your subqueries (especially in the FROM clause). - Avoid correlated subqueries if possible; they’re slower. React with ❤️ if you want me to cover the next topic: 🏷 Aliases & Case Statements.

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🔗 SQL JOINS (INNER, LEFT, RIGHT, FULL, SELF) JOINS help you combine data from two or more tables based on a related column (usually a primary key and a foreign key). 1. INNER JOIN Returns only matching rows between two tables. SELECT customers.name, orders.order_id FROM customers INNER JOIN orders ON customers.id = orders.customer_id; This returns only those customers who have placed at least one order. 2. LEFT JOIN (or LEFT OUTER JOIN) Returns all rows from the left table, and matched rows from the right table. If no match, you'll see NULLs. SELECT customers.name, orders.order_id FROM customers LEFT JOIN orders ON customers.id = orders.customer_id; This shows all customers, including those who haven’t placed any orders. 3. RIGHT JOIN (or RIGHT OUTER JOIN) Returns all rows from the right table, and matching rows from the left. SELECT customers.name, orders.order_id FROM customers RIGHT JOIN orders ON customers.id = orders.customer_id; You’ll see all orders — even if there’s no corresponding customer info. 4. FULL JOIN (or FULL OUTER JOIN) Returns all rows from both tables. If there's no match, it returns NULLs. Note: MySQL doesn't support FULL JOIN directly; use UNION of LEFT and RIGHT joins instead. 5. SELF JOIN You join a table with itself. Great for hierarchical relationships. SELECT e.name AS employee, m.name AS manager FROM employees e JOIN employees m ON e.manager_id = m.id; This shows each employee along with their manager's name. Pro Tip: Be careful with NULLs and always define clear join conditions to avoid cartesian products. React with ❤️ if you're ready for the next one: 📦 Subqueries & Nested Queries. Share with credits: https://t.me/sqlspecialist Hope it helps :)

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👥 GROUP BY & HAVING Clauses 1. GROUP BY GROUP BY is used to group rows that have the same values in specified columns and apply aggregate functions to each group. Syntax: SELECT column, AGG_FUNC(column2) FROM table_name GROUP BY column; Example: SELECT department, COUNT(*) AS total_employees FROM employees GROUP BY department; This shows how many employees are in each department. You can group by multiple columns too: SELECT department, job_title, AVG(salary) AS avg_salary FROM employees GROUP BY department, job_title; 2. HAVING HAVING is like WHERE, but it’s used to filter grouped data. You can't use WHERE with aggregate functions — that's where HAVING comes in. Example: SELECT department, COUNT(*) AS total_employees FROM employees GROUP BY department HAVING COUNT(*) > 5; This gives you only those departments that have more than 5 employees. Bonus: Combine GROUP BY + ORDER BY + HAVING: SELECT category, SUM(sales) AS total_sales FROM products GROUP BY category HAVING SUM(sales) > 10000 ORDER BY total_sales DESC; This gives you the top-selling categories with sales over 10,000. React with ❤️ if you’re ready for the next banger: 🔗 SQL JOINS (INNER, LEFT, RIGHT, FULL, SELF). Share with credits: https://t.me/sqlspecialist Hope it helps :)