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Glad to see the amazing response on SQL roadmap. ❤️

Today, let's start with the first topic of SQL roadmap:

✅ Introduction to SQL

SQL is the language you use to ask questions from data stored in databases. Companies store all important data in databases. Sales. Users. Payments. Inventory. When a manager asks a question, SQL pulls the answer.

What a database is
A database is an organized storage system for data. Think of it as a digital cupboard where each drawer holds related data. Each drawer is called a table.

What a table is
A table looks like an Excel sheet. It has rows and columns. Each table stores one type of data.

Example table: customers
- Columns
- customer_id
- name
- email
- city
- signup_date
- Rows
- Each row represents one customer
- One row equals one real-world record

How rows and columns work together
- Columns define what kind of data you store
- Rows hold actual values
- All rows follow the same column structure

Example row
- customer_id: 101
- name: Rahul
- email: rahul@gmail.com
- city: Pune
- signup_date: 2024-03-10

Why structure matters
- Clean structure makes data reliable
- Easy to filter, count, and analyze
- Required for accurate reporting

How SQL interacts with tables
- SQL reads data from tables
- SQL filters rows
- SQL selects columns
- SQL summarizes results

Simple SQL example
You ask the database to show names and cities of customers.

SELECT name, city FROM customers;

What happens behind the scenes
- Database scans the customers table
- Picks name and city columns
- Returns matching rows

Where you will use this daily
- Pull user lists
- Check sales numbers
- Validate data issues

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Glad to see the amazing response on SQL roadmap. ❤️

Today, let's move to the next topic of SQL roadmap:

✅ Database Basics: Primary Key and Foreign Key

Why Keys Exist
Databases store millions of rows. Keys help identify and connect data correctly. Without keys, data breaks fast.

Primary Key
- A primary key uniquely identifies each row in a table
- No two rows share the same primary key
- It never stays empty

Example Table: customers
- Columns: customer_id, name, email, city
- Primary key: customer_id

Why Primary Key Matters
- Prevents duplicate records
- Helps find a row fast
- Keeps data consistent

Foreign Key
- A foreign key links one table to another
- It creates relationships between tables

Example Table: orders
- Columns: order_id, customer_id, order_date, amount
- Foreign key: customer_id

Relationship Explained
- customers.customer_id is primary key
- orders.customer_id is foreign key
- One customer has many orders

Why Foreign Keys Matter
- Enable joins
- Prevent orphan records
- Maintain data integrity

Simple Join Idea
SQL matches customer_id in both tables. This gives customer name with order amount.

Where Beginners Go Wrong
- Using names instead of IDs
- Allowing duplicate primary keys
- Ignoring missing foreign key values

Checkpoint
- You understand primary keys
- You understand foreign keys
- You know how tables connect

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Today, let's move to the next topic of SQL Roadmap:

Basic SQL Queries: SELECT, WHERE, Filtering 🖥️

What SELECT Does
- SELECT chooses columns
- You decide what data you want to see
- Database returns only those columns

Example Table: customers
customer_id | name | city | signup_date
101 | Rahul | Pune | 2024-01-15
102 | Neha | Mumbai | 2024-02-10
103 | Amit | Delhi | 2024-03-05
104 | Priya | Pune | 2024-04-20

Basic SELECT

SELECT name, city FROM customers;

Output
name | city
Rahul | Pune
Neha | Mumbai
Amit | Delhi
Priya | Pune

What Happens
- Database scans all rows
- Returns name and city columns
- No filtering yet

Why SELECT Matters
- Smaller output
- Faster queries
- Clear analysis

What WHERE Does
- WHERE filters rows
- It answers conditions like who, when, how much

Think Like This
- SELECT decides columns
- WHERE decides rows

Basic WHERE Example

SELECT name, city FROM customers WHERE city = 'Pune';

Output
name | city
Rahul | Pune
Priya | Pune

Common Operators
- = equal
- != not equal
- > greater than
- < less than
- > =, <=

Example

SELECT name, signup_date FROM customers WHERE signup_date >= '2024-03-01';

Output
name | signup_date
Amit | 2024-03-05
Priya | 2024-04-20

Logical Filters
AND
- All conditions must match
Example: SELECT name FROM customers WHERE city = 'Pune' AND signup_date >= '2024-01-01';

Output
name
Rahul
Priya

OR
- Any condition can match
Example: SELECT name FROM customers WHERE city = 'Pune' OR city = 'Mumbai';

Output
name
Rahul
Neha
Priya

IN
- Shortcut for multiple OR conditions
Example: SELECT name FROM customers WHERE city IN ('Pune','Mumbai','Delhi');

Output
name
Rahul
Neha
Amit
Priya

BETWEEN
- Filters within a range
- Inclusive of start and end
Example: SELECT name FROM customers WHERE signup_date BETWEEN '2024-01-01' AND '2024-03-31';

Output
name
Rahul
Neha
Amit

Filtering Numbers
Example table: orders
order_id | customer_id | amount | order_date
1 | 101 | 3000 | 2024-01-16
2 | 102 | 6000 | 2024-02-11
3 | 103 | 4000 | 2024-03-06

SELECT order_id, amount FROM orders WHERE amount > 5000;

Output
order_id | amount
2 | 6000

Filtering Text
- Text values go inside quotes
- Case sensitivity depends on database

Filtering NULL Values
- NULL means missing value
- = NULL does not work
Correct way: SELECT name FROM customers WHERE city IS NULL;

Assume city is NULL for customer_id 103
Output
name
Amit

Exclude NULL: SELECT name FROM customers WHERE city IS NOT NULL;

Output
name
Rahul
Neha
Priya

How SELECT and WHERE Work Together
- FROM picks the table
- WHERE filters rows
- SELECT picks columns
- Result is sent back

Real Business Example
- Manager asks for Pune customers with orders above 5,000
- WHERE applies city and amount filters
- SELECT shows required columns

Assume orders table has customer_id 101 with amount 6000

SELECT name FROM customers WHERE city = 'Pune' AND customer_id IN (SELECT customer_id FROM orders WHERE amount > 5000);

Output
name
Rahul

Common Beginner Mistakes
- Using WHERE before FROM
- Forgetting quotes for text
- Using = NULL
- Writing SELECT * always

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Today, let's move to the next topic of SQL Roadmap:

✅ Basic SQL Queries: ORDER BY and LIMIT - Sorting and Controlling Output

Why Sorting
- Raw data has no order
- Sorted data reveals patterns
- Analysts sort data in almost every query

What ORDER BY Does
- ORDER BY sorts result rows
- Sorting happens after filtering
- Default order is ascending

Basic Syntax

SELECT column_name
FROM table_name
ORDER BY column_name;

Example Table: customers
| customer_id | name | city | signup_date |

Sort by Name

SELECT name, city
FROM customers
ORDER BY name;

- Rows sorted alphabetically by name (A to Z)

Descending Order
- Use DESC for reverse order

SELECT name, signup_date
FROM customers
ORDER BY signup_date DESC;

Use Cases
- Latest users first
- Highest sales first
- Recent transactions on top

Sorting Numbers

SELECT order_id, amount
FROM orders
ORDER BY amount DESC;

Sorting by Multiple Columns
- First column sorts primary
- Second column breaks ties

SELECT city, signup_date
FROM customers
ORDER BY city, signup_date DESC;

LIMIT Explained
- LIMIT restricts number of rows returned
- Used to preview data
- Used to get top results

SELECT name, signup_date
FROM customers
ORDER BY signup_date DESC
LIMIT 5;

Top N Queries

SELECT name, amount
FROM orders
ORDER BY amount DESC
LIMIT 10;

Filtering + Sorting + Limiting Together
Execution order:
1. FROM
2. WHERE
3. ORDER BY
4. LIMIT

SELECT name, amount
FROM orders
WHERE amount > 5000
ORDER BY amount DESC
LIMIT 3;

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Today, let's move to the next topic of SQL Roadmap:

✅ Basic SQL Queries: Aggregations and GROUP BY

• Why aggregations matter
• Raw rows hide patterns
• Businesses care about totals, averages, counts
• Aggregations turn rows into answers

• Common aggregate functions
• COUNT: counts rows
• SUM: adds values
• AVG: finds average
• MIN: finds smallest value
• MAX: finds largest value

• Examples
SELECT COUNT(*) FROM orders;  -- total orders 
SELECT SUM(amount) FROM orders;  -- total revenue 
SELECT AVG(amount) FROM orders;  -- average order value 
SELECT MIN(amount), MAX(amount) FROM orders;  -- spend range 

• GROUP BY
• Groups rows by a column
• Applies aggregation per group
• One result per group
SELECT customer_id, SUM(amount) FROM orders GROUP BY customer_id;  -- total spend per customer 
SELECT order_date, COUNT(*) FROM orders GROUP BY order_date;  -- daily order volume 

• Important rule
Every column in SELECT must be aggregated or present in GROUP BY.

• Using WHERE with GROUP BY
SELECT customer_id, SUM(amount) FROM orders
WHERE amount > 5000
GROUP BY customer_id;  -- high-value orders per customer 

Real business use:
• Revenue per customer
• Orders per day
• Average order value per city

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Today, let's move to the next topic of SQL Roadmap:

✅ HAVING vs WHERE

Why HAVING exists

- WHERE filters individual rows.
- Aggregations work on groups.
- You need HAVING to filter groups.

Key difference
- WHERE filters rows before grouping
- HAVING filters groups after aggregation

Think in order
- FROM reads table
- WHERE filters rows
- GROUP BY creates groups
- HAVING filters groups

Example table. orders
order_id | customer_id | amount | order_date

WHERE with aggregation

SELECT customer_id, SUM(amount)
FROM orders
WHERE amount > 5000
GROUP BY customer_id;

What this query does
- Removes orders below 5,000
- Groups remaining orders by customer
- Calculates total high value spend per customer

HAVING example

SELECT customer_id, SUM(amount)
FROM orders
GROUP BY customer_id
HAVING SUM(amount) > 20000;

What this query does
- Groups all orders by customer
- Calculates total spend per customer
- Keeps only customers with total spend above 20,000

WHERE vs HAVING side by side

Use WHERE when
- Filtering raw rows
- Condition does not involve aggregate functions

Use HAVING when
- Filtering aggregated results
- Condition uses SUM, COUNT, AVG

Combined WHERE and HAVING

SELECT customer_id, COUNT(*)
FROM orders
WHERE order_date >= '2024-01-01'
GROUP BY customer_id
HAVING COUNT(*) >= 5;

What this query does
- Keeps orders from 2024 onward
- Groups orders by customer
- Counts orders per customer
- Returns customers with at least 5 orders

Common beginner mistakes
- Using HAVING without GROUP BY
- Using WHERE with SUM or COUNT
- Mixing WHERE and HAVING logic

Interview one-liner
WHERE filters rows. HAVING filters groups.

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Today, let's move to the next topic of SQL Roadmap:

✅ SQL JOINS

What a JOIN is
• A JOIN combines data from two or more tables
• Tables connect using a common column
• That column is usually an ID
• JOIN answers questions one table cannot answer

Why JOINs exist
• Customer details sit in one table
• Orders sit in another table
• JOIN links customers to their orders

Example tables
customers 
customer_id | name | city 
orders 
order_id | customer_id | amount 

Connection
• customers.customer_id is primary key
• orders.customer_id is foreign key
• This shared column enables JOIN

Types of JOINs you must know
• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• FULL JOIN
• SELF JOIN

INNER JOIN
• Returns only matching rows from both tables
• Drops anything without a match

SELECT name, amount 
FROM customers 
INNER JOIN orders 
ON customers.customer_id = orders.customer_id;

• Matches customers with their orders
• Shows only customers who placed orders
• Removes customers with no orders
• Removes orders without customers

LEFT JOIN
• Returns all rows from left table
• Matches data from right table
• Shows NULL when no match exists

SELECT name, amount 
FROM customers 
LEFT JOIN orders 
ON customers.customer_id = orders.customer_id;

• Returns every customer
• Shows order amount if available
• Shows NULL if customer never ordered

RIGHT JOIN
• Returns all rows from right table
• Matches data from left table
• Opposite of LEFT JOIN

SELECT name, amount 
FROM customers 
RIGHT JOIN orders 
ON customers.customer_id = orders.customer_id;

• Returns all orders
• Shows customer name if exists
• Shows NULL for missing customer data

FULL JOIN
• Returns all rows from both tables
• Matches where possible
• Shows NULL when no match

SELECT name, amount 
FROM customers 
FULL JOIN orders 
ON customers.customer_id = orders.customer_id;

• Shows all customers
• Shows all orders
• Includes unmatched data from both sides

SELF JOIN
• Table joins with itself
• Used for hierarchy or comparison

SELECT e.name, m.name AS manager_name 
FROM employees e 
LEFT JOIN employees m 
ON e.manager_id = m.employee_id;

• Matches employee with manager
• Uses same table twice
• Shows reporting hierarchy

JOIN Comparison Summary
• INNER JOIN: Only matching data
• LEFT JOIN: All left table rows
• RIGHT JOIN: All right table rows
• FULL JOIN: Everything from both tables
• SELF JOIN: Table joins itself

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If you want to Excel at using the most used database language in the world, learn these powerful SQL features:

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• Window Functions – ROW_NUMBER(), RANK(), DENSE_RANK(), LEAD(), LAG()
• Common Table Expressions (CTEs) – WITH for better readability
• Recursive Queries – Handle hierarchical data
• STRING Functions – LEFT(), RIGHT(), LEN(), TRIM(), UPPER(), LOWER()
• Date Functions – DATEDIFF(), DATEADD(), FORMAT()
• Pivot & Unpivot – Transform row data into columns
• Aggregate Functions – SUM(), AVG(), COUNT(), MIN(), MAX()
• Joins & Self Joins – Master INNER, LEFT, RIGHT, FULL, SELF JOIN
• Indexing – Speed up queries with CREATE INDEX

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✅ SQL JOINS — Scenario-Based Interview Questions with Answers

Scenario 1: Find customers who have never placed an order

Tables: customers(customer_id, name) orders(order_id, customer_id);

Question: Business wants a list of customers with zero orders.

Answer:

SELECT c.customer_id, c.name FROM customers c LEFT JOIN orders o ON c.customer_id = o.customer_id WHERE o.customer_id IS NULL;

Why this works
• LEFT JOIN keeps all customers
• Orders missing → NULL
• WHERE filters only non-ordering customers

Scenario 2: Get total revenue per customer, including customers with no orders

Question: Show every customer and their total spend. If no orders, show 0.

Answer:

SELECT c.customer_id, c.name, COALESCE(SUM(o.amount), 0) AS total_spend FROM customers c LEFT JOIN orders o ON c.customer_id = o.customer_id GROUP BY c.customer_id, c.name;

Explanation
• LEFT JOIN keeps all customers
• SUM aggregates orders
• COALESCE converts NULL to 0

Scenario 3: Find orders that don’t have a matching customer

Question: Audit data to find orphan orders.

Answer:

SELECT o.order_id, o.customer_id FROM orders o LEFT JOIN customers c ON o.customer_id = c.customer_id WHERE c.customer_id IS NULL;

Explanation
• LEFT JOIN from orders
• Missing customers become NULL
• Filters invalid data

Scenario 4: Get only customers who have placed at least one order

Question: Marketing wants only active customers.

Answer:

SELECT DISTINCT c.customer_id, c.name FROM customers c INNER JOIN orders o ON c.customer_id = o.customer_id;

Explanation
• INNER JOIN keeps only matching rows
• Customers without orders are excluded

Scenario 5: Find customers with more than 3 orders

Answer:

SELECT c.customer_id, c.name, COUNT(o.order_id) AS order_count FROM customers c INNER JOIN orders o ON c.customer_id = o.customer_id GROUP BY c.customer_id, c.name HAVING COUNT(o.order_id) > 3;

Explanation
• JOIN combines data
• GROUP BY customer
• HAVING filters aggregated count

Scenario 6: Show latest order for each customer

Answer:

SELECT c.customer_id, c.name, MAX(o.order_date) AS last_order_date FROM customers c INNER JOIN orders o ON c.customer_id = o.customer_id GROUP BY c.customer_id, c.name;

Explanation
• JOIN connects customers and orders
• MAX finds latest order per customer

Scenario 7: Find customers who ordered in 2024 but not in 2025

Answer:

SELECT DISTINCT c.customer_id, c.name FROM customers c INNER JOIN orders o2024 ON c.customer_id = o2024.customer_id LEFT JOIN orders o2025 ON c.customer_id = o2025.customer_id AND o2025.order_date >= '2025-01-01' WHERE o2024.order_date BETWEEN '2024-01-01' AND '2024-12-31' AND o2025.customer_id IS NULL;

Explanation
• INNER JOIN ensures 2024 orders
• LEFT JOIN checks absence in 2025
• NULL filter removes 2025 buyers

Scenario 8: Employee-Manager hierarchy (SELF JOIN)

Table: employees(employee_id, name, manager_id);

Answer:

SELECT e.name AS employee, m.name AS manager FROM employees e LEFT JOIN employees m ON e.manager_id = m.employee_id;

Explanation
• Same table joined twice
• Shows reporting structure

Scenario 9: Revenue by city

Answer:

SELECT c.city, SUM(o.amount) AS revenue FROM customers c INNER JOIN orders o ON c.customer_id = o.customer_id GROUP BY c.city;

Explanation
• JOIN links customers to orders
• GROUP BY city
• SUM calculates revenue

Scenario 10: Duplicate explosion after JOIN (classic trap)

Question: Why does this query show inflated revenue?

SELECT SUM(o.amount) FROM customers c JOIN orders o ON c.customer_id = o.customer_id;

Answer:
• Customer table may have duplicates
• JOIN multiplies rows
• Revenue gets inflated

Fix:

SELECT SUM(amount) FROM orders; or deduplicate customers before joining.

Interview golden rule for JOINS
Always explain:
1️⃣ Which table is LEFT
2️⃣ Which table is RIGHT
3️⃣ What rows are kept
4️⃣ Where NULLs appear

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SQL Detailed Roadmap
|
| | |-- Fundamentals
| |-- Introduction to Databases
| | |-- What SQL does
| | |-- Relational model
| | |-- Tables, rows, columns
| |-- Keys and Constraints
| | |-- Primary keys
| | |-- Foreign keys
| | |-- Unique and check constraints
| |-- Normalization
| | |-- 1NF, 2NF, 3NF
| | |-- ER diagrams

| | |-- Core SQL
| |-- SQL Basics
| | |-- SELECT, WHERE, ORDER BY
| | |-- GROUP BY and HAVING
| | |-- JOINS: INNER, LEFT, RIGHT, FULL
| |-- Intermediate SQL
| | |-- Subqueries
| | |-- CTEs
| | |-- CASE statements
| | |-- Aggregations
| |-- Advanced SQL
| | |-- Window functions
| | |-- Analytical functions
| | |-- Ranking, moving averages, lag and lead
| | |-- UNION, INTERSECT, EXCEPT

| | |-- Data Management
| |-- Data Types
| | |-- Numeric, text, date, JSON
| |-- Indexes
| | |-- B tree and hash indexes
| | |-- When to create indexes
| |-- Transactions
| | |-- ACID properties
| |-- Views
| | |-- Standard views
| | |-- Materialized views

| | |-- Database Design
| |-- Schema Design
| | |-- Star schema
| | |-- Snowflake schema
| |-- Fact and Dimension Tables
| |-- Constraints for clean data

| | |-- Performance Tuning
| |-- Query Optimization
| | |-- Execution plans
| | |-- Index usage
| | |-- Reducing scans
| |-- Partitioning
| | |-- Horizontal partitioning
| | |-- Sharding basics

| | |-- SQL for Analytics
| |-- KPI calculations
| |-- Cohort analysis
| |-- Funnel analysis
| |-- Churn and retention tables
| |-- Time based aggregations
| |-- Window functions for metrics

| | |-- SQL for Data Engineering
| |-- ETL Workflows
| | |-- Staging tables
| | |-- Transformations
| | |-- Incremental loads
| |-- Data Warehousing
| | |-- Snowflake
| | |-- Redshift
| | |-- BigQuery
| |-- dbt Basics
| | |-- Models
| | |-- Tests
| | |-- Lineage

| | |-- Tools and Platforms
| |-- PostgreSQL
| |-- MySQL
| |-- SQL Server
| |-- Oracle
| |-- SQLite
| |-- Cloud SQL
| |-- BigQuery UI
| |-- Snowflake Worksheets

| | |-- Projects
| |-- Build a sales reporting system
| |-- Create a star schema from raw CSV files
| |-- Design a customer segmentation query
| |-- Build a churn dashboard dataset
| |-- Optimize slow queries in a sample DB
| |-- Create an analytics pipeline with dbt

| | |-- Soft Skills and Career Prep
| |-- SQL interview patterns
| |-- Joins practice
| |-- Window function drills
| |-- Query writing speed
| |-- Git and GitHub
| |-- Data storytelling

| | |-- Bonus Topics
| |-- NoSQL intro
| |-- Working with JSON fields
| |-- Spatial SQL
| |-- Time series tables
| |-- CDC concepts
| |-- Real time analytics

| | |-- Community and Growth
| |-- LeetCode SQL
| |-- Kaggle datasets with SQL
| |-- GitHub projects
| |-- LinkedIn posts
| |-- Open source contributions

Free Resources to learn SQL

• W3Schools SQL
https://www.w3schools.com/sql/

• SQL Programming
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

• SQL Notes
https://whatsapp.com/channel/0029Vb6hJmM9hXFCWNtQX944

• Mode Analytics SQL tutorials
https://mode.com/sql-tutorial/

• Data Analytics Resources
https://news.1rj.ru/str/sqlspecialist

• HackerRank SQL practice
https://www.hackerrank.com/domains/sql

• LeetCode SQL problems
https://leetcode.com/problemset/database/

• Data Engineering Resources
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

• Khan Academy SQL basics
https://www.khanacademy.org/computing/computer-programming/sql

• PostgreSQL official docs
https://www.postgresql.org/docs/

• MySQL official docs
https://dev.mysql.com/doc/

• NoSQL Resources
https://whatsapp.com/channel/0029VaxA2hTHgZWe5FpFjm3p

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