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Day 1 — Tables, Rows & Thinking in Data
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Day 1 — Tables, Rows & Thinking in Data

⁠

🟢 Day 1 — Tables, Rows & Thinking in Data

🎯 Day 1 Outcome

By the end of Day 1, the student should be able to:
Understand what a table really represents
Design a basic schema correctly
Insert and fetch data using SQL
Think in rows, not files
Answer why a schema is designed in a certain way
Today is not about SQL syntax. Today is about how data lives.
⁠

1️⃣ How to Think About Databases (Teaching Script)

Start with This Question

“If I restart my backend server, where does my data go?”
Answer:
Variables → gone ❌
Lists / dicts → gone ❌
Database → still there ✅
A database is long-term memory for software.
⁠

2️⃣ What Is a Table? (Very Important)

Wrong Mental Model ❌

“Table is like Excel”

Correct Mental Model ✅

A table stores many objects of the same type.
Example:
User table = many users
Order table = many orders
⁠

Mapping Real World → Table

Real World
Database
One user
One row
User attributes
Columns
User identity
Primary key
There are no rows in this table
⁠
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3️⃣ Rows, Columns & Primary Key

Example: User Entity

In real life, a user has:
id
name
email
created time
So the table becomes:
CREATE TABLE users (
user_id INT PRIMARY KEY,
name VARCHAR(100),
email VARCHAR(100),
created_at TIMESTAMP
);
⁠

Explain Line by Line (Student Must Read)

users → collection of users
user_id → unique identity
PRIMARY KEY → cannot be duplicate, cannot be NULL
VARCHAR(100) → text with limit
created_at → when user was created
🧠 If you don’t have a primary key, you don’t really have a table.
⁠

4️⃣ Why Not Use Email as Primary Key? (Interview Thinking)

Ask the student:
Can email change? → Yes
Can user have multiple emails? → Possibly
Is email long? → Yes
So:
Primary key should be stable, small, and meaningless.
That’s why IDs exist.
⁠

5️⃣ Basic SQL Operations (Must Know)

Insert Data

INSERT INTO users (user_id, name, email, created_at)
VALUES (1, 'Amit', 'amit@gmail.com', '2024-01-01');
Each INSERT = one row
⁠

Fetch All Data

SELECT * FROM users;
Think: “Give me all users”
⁠

Fetch Specific Columns

SELECT name, email FROM users;
Don’t fetch unnecessary data (interviewers notice this)
⁠

Fetch by ID (Most Common Query)

SELECT * FROM users WHERE user_id = 1;
This should return at most one row (because primary key is unique)
⁠

6️⃣ Filtering Data with WHERE

SELECT * FROM users
WHERE created_at >= '2024-01-01';
Read in English:
“Give me users created after Jan 1, 2024”
⁠

7️⃣ Counting Rows (Extremely Common)

SELECT COUNT(*) FROM users;
“How many users exist?”
⁠

8️⃣ Common Beginner Mistakes (Read Carefully)

❌ No primary key ❌ Using email/name as primary key ❌ Fetching all columns always ❌ Thinking table = file
⁠

9️⃣ Practice Problems (DO THESE)

Problem 1 — Create Table

Create a table employees with:
emp_id
name
department
joining_date
⁠

Problem 2 — Insert Data

Insert 3 employees into table.
⁠

Problem 3 — Fetch Queries

Write queries to:
Fetch all employees
Fetch only names
Fetch employee with emp_id = 2
Fetch employees from a specific department
⁠

Problem 4 — Count

Count how many employees exist.
⁠

🔍 HINTS (Read Only If Stuck)

Use PRIMARY KEY
Use WHERE for filtering
Use COUNT(*) for counting
SQL reads like English (left → right)
⁠

✅ Solutions (Read After Attempting)

Solution 1 — Table Creation

CREATE TABLE employees (
emp_id INT PRIMARY KEY,
name VARCHAR(100),
department VARCHAR(50),
joining_date DATE
);
⁠

Solution 2 — Insert

INSERT INTO employees VALUES
(1, 'Rahul', 'Engineering', '2022-01-10'),
(2, 'Neha', 'HR', '2023-03-15'),
(3, 'Amit', 'Engineering', '2021-06-20');
⁠

Solution 3 — Queries

SELECT * FROM employees;

SELECT name FROM employees;

SELECT * FROM employees WHERE emp_id = 2;

SELECT * FROM employees WHERE department = 'Engineering';
⁠

Solution 4 — Count

SELECT COUNT(*) FROM employees;
⁠

🧠 Reflection Questions (Must Answer)

Why do we need a primary key?
Why not store multiple users in one row?
What happens if two rows have same emp_id?
Why should IDs not carry meaning?
⁠

⭐ Day 1 Golden Rule

Databases store facts, not logic. Logic lives in code.
⁠

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