Tables, rows, and columns concept in SQL

Introduction

Tables organize data in a simple way using rows and columns. This helps us store and find information easily.

When you want to save a list of customers with their details.

When you need to keep track of products and their prices.

When you want to record daily sales data.

When you want to organize employee information like names and job titles.

Syntax

SQL

CREATE TABLE table_name (
  column1 datatype,
  column2 datatype,
  column3 datatype
);

A table is like a spreadsheet with rows and columns.

Each row is one record or entry.

Each column holds one type of information, like a name or number.

Examples

This creates a table named Customers with three columns: an ID number, a name, and a city.

SQL

CREATE TABLE Customers (
  CustomerID INT,
  CustomerName VARCHAR(100),
  City VARCHAR(50)
);

This table stores product details including an ID, name, and price with two decimal places.

SQL

CREATE TABLE Products (
  ProductID INT,
  ProductName VARCHAR(100),
  Price DECIMAL(10,2)
);

Sample Program

This example creates an Employees table, adds two employees, and then shows all the data in the table.

SQL

CREATE TABLE Employees (
  EmployeeID INT,
  FirstName VARCHAR(50),
  LastName VARCHAR(50),
  JobTitle VARCHAR(50)
);

INSERT INTO Employees VALUES (1, 'Alice', 'Smith', 'Manager');
INSERT INTO Employees VALUES (2, 'Bob', 'Brown', 'Sales');

SELECT * FROM Employees;

Important Notes

Each row must have a value for every column unless the column allows empty values.

Column names should be clear to understand what data they hold.

Summary

Tables store data in rows and columns.

Rows are individual records.

Columns define the type of data stored.

Practice

1. What does a row represent in a database table?

easy

A. A group of tables linked together

B. The name of the table

C. The type of data stored in the table

D. A single record containing data for each column

Solution

1. Step 1: Understand the structure of a table

   A table is made up of rows and columns where columns define data types and rows hold data.

2. Step 2: Identify what a row represents

   A row is one complete record with values for each column in the table.

3. Final Answer:

   A single record containing data for each column -> Option D

4. Quick Check:

   Row = single record [OK]

Hint: Rows = records, columns = data types [OK]

Common Mistakes:

• Confusing rows with columns
• Thinking rows are table names
• Mixing up rows with database schemas

2. Which of the following is the correct way to select all columns from a table named Employees?

easy

A. GET * FROM Employees;

B. SELECT ALL COLUMNS Employees;

C. SELECT * FROM Employees;

D. SELECT COLUMNS FROM Employees;

Solution

1. Step 1: Recall SQL syntax for selecting all columns

   The standard SQL command to select all columns is SELECT * FROM table_name;.

2. Step 2: Match the correct syntax

   SELECT * FROM Employees; matches the correct syntax exactly.

3. Final Answer:

   SELECT * FROM Employees; -> Option C

4. Quick Check:

   SELECT * = all columns [OK]

Hint: Use * to select all columns [OK]

Common Mistakes:

• Using GET instead of SELECT
• Writing SELECT ALL COLUMNS which is invalid
• Omitting FROM keyword

3. Given the table Students with columns id, name, and age, what will this query return?

SELECT name FROM Students WHERE age > 20;

medium

A. Only the names of students older than 20

B. All columns for students older than 20

C. Only the ages of students older than 20

D. An error because age is not selected

Solution

1. Step 1: Analyze the SELECT clause

   The query selects only the name column from the table.

2. Step 2: Analyze the WHERE condition

   It filters rows where age is greater than 20, so only those students are included.

3. Final Answer:

   Only the names of students older than 20 -> Option A

4. Quick Check:

   SELECT name WHERE age > 20 = names only [OK]

Hint: SELECT columns control output, WHERE filters rows [OK]

Common Mistakes:

• Thinking all columns are returned
• Assuming age column is returned without selecting it
• Believing query causes error due to WHERE clause

4. What is wrong with this SQL query?

SELECT id, name FROM Students WHERE age = 'twenty';

medium

A. Using quotes around a number in WHERE clause causes error

B. Comparing age to a string 'twenty' may cause no matches

C. Selecting multiple columns requires commas

D. Missing FROM keyword before table name

Solution

1. Step 1: Check the WHERE clause comparison

   The query compares age to the string 'twenty', but age is numeric.

2. Step 2: Understand effect of comparing number to string

   This will not cause a syntax error but likely returns zero rows because no numeric age equals 'twenty'.

3. Final Answer:

   Comparing age to a string 'twenty' may cause no matches -> Option B

4. Quick Check:

   Wrong data type comparison = no results [OK]

Hint: Match data types in WHERE clause [OK]

Common Mistakes:

• Thinking quotes cause syntax error
• Assuming query returns matching rows
• Ignoring data type mismatch

5. You have a table Orders with columns order_id, customer_id, and amount. You want to find the total amount spent by each customer. Which SQL query correctly groups and sums the data?

hard

A. SELECT customer_id, SUM(amount) FROM Orders GROUP BY customer_id;

B. SELECT order_id, SUM(amount) FROM Orders GROUP BY order_id;

C. SELECT customer_id, amount FROM Orders WHERE SUM(amount) > 100;

D. SELECT customer_id, amount FROM Orders GROUP BY amount;

Solution

1. Step 1: Identify the goal

   We want total amount spent per customer, so we must group by customer_id.

2. Step 2: Use aggregation function

   SUM(amount) calculates total amount per group.

3. Step 3: Check query correctness

   SELECT customer_id, SUM(amount) FROM Orders GROUP BY customer_id; correctly groups by customer_id and sums amount.

4. Final Answer:

   SELECT customer_id, SUM(amount) FROM Orders GROUP BY customer_id; -> Option A

5. Quick Check:

   Group by customer_id + SUM(amount) = total per customer [OK]

Hint: Group by key, aggregate values with SUM [OK]

Common Mistakes:

• Grouping by wrong column
• Using WHERE with aggregate functions incorrectly
• Selecting columns not in GROUP BY without aggregation