Once upon a time in the Land of Datapolis, a young data architect named Aarav was given a mighty mission:

“Design the data system for the Kingdom’s growing business empire.”

He had all the power—he could create any table, name any column, and define how all the data in the land would be stored. But his mentor, an old master named Sir Schema, gave him one golden rule:

“The name you give is the soul of the data. Choose carelessly, and confusion will reign.”

Chapter 1: The First Mistake – The Table of Chaos

Aarav created his first table like this:

CREATE TABLE Xyz123 (
    a1 INT,
    b2 VARCHAR(50),
    c3 DATE
);

It worked… but no one knew what it meant.

Business Analyst:

“What’s in b2?”

Developer:

“Why is it called Xyz123? Is it for products or invoices?”

Aarav had built a data dungeon, not a data kingdom.

Chapter 2: Naming with Intention

Sir Schema smiled and said:

“Let your table names speak for themselves. Let columns tell a story.”

Aarav started over.

CREATE TABLE CustomerOrders (
    OrderID INT PRIMARY KEY,
    CustomerID INT,
    OrderDate DATE,
    TotalAmount DECIMAL(10,2)
);

Now, anyone could understand the table’s purpose just by reading it.

• CustomerOrders: Clearly a list of purchases by customers
• OrderDate: No confusion about what the date means
• TotalAmount: Tells exactly what the number represents

Aarav realized:

“Naming isn’t for the computer. It’s for every human who will ever read this.”

Chapter 3: Rules for a Kingdom of Clarity

Aarav set some sacred rules that everyone followed:

1. Use Singular Nouns for Table Names
   
   • Customer, not Customers
   • Invoice, not Invoices
   • Because each row is one item
2. Name for Meaning, Not Convenience
   
   • Use CreatedDate, not just Date
   • Use ProductPrice, not just Price (context matters!)
3. Stick to Consistent Naming Patterns
   
   • FirstName, LastName, PhoneNumber
   • Avoid mixing styles like first_name, LastName, phoneNo
4. Avoid Reserved Words and Shortcuts
   
   • Don’t name a table Order (it’s a reserved SQL word)
   • Avoid lazy names like info, data, temp
5. Use Prefixes or Suffixes When It Adds Value
   
   • UserID, CustomerID (foreign keys)
   • IsActive, HasEmail for boolean flags

Chapter 4: The Long-Term Win

Years passed. New analysts, engineers, and even interns joined the Kingdom.

They read Aarav’s tables and instantly understood what each field meant.

They built dashboards, wrote queries, and made decisions without guessing.

Because Aarav had named everything with clarity, there was:

• Less confusion
• Fewer bugs
• Faster onboarding
• Happier teams

His data system became the standard for the entire land.

Chapter 5: The Naming Checklist of Champions

Before creating any table or column, Aarav taught everyone to ask:

• What real-world thing does this represent?
• Would a non-technical person understand the name?
• Is the name too short, vague, or misleading?
• Will this still make sense a year from now?
• Are we following our naming conventions?

Conclusion: A Name is a Gift to the Future

Aarav’s story teaches us this:

Every column and table you create is a message to the next person. Will it guide them—or confuse them?

In the world of databases, smart naming is kindness, clarity, and power all at once.

Meet Suvan, a young entrepreneur opening a small café. He wants to track all his menu items and sales digitally. So, he decides to build his first database.

And like every good recipe, it starts with the right structure.

Chapter 1: Creating the Table – Designing the Blueprint

Suvan:

“I need a place to store all my dishes—name, price, and category.”

Enter SQL.

He creates a table called Menu:

CREATE TABLE Menu (
    DishID INT PRIMARY KEY,
    DishName VARCHAR(50),
    Category VARCHAR(20),
    Price DECIMAL(5,2)
);

What does this mean?

• DishID is a number that uniquely identifies each dish.
• DishName holds text up to 50 characters.
• Category helps organize items (e.g., ‘Dessert’, ‘Drink’).
• Price allows for decimals, like 12.50.

Chapter 2: Inserting Data – Adding Dishes to the Menu

Now that the table is ready, Suvan starts adding dishes:

INSERT INTO Menu (DishID, DishName, Category, Price)
VALUES (1, 'Masala Dosa', 'Main Course', 7.99);

He adds a few more:

INSERT INTO Menu VALUES 
(2, 'Filter Coffee', 'Beverage', 2.50),
(3, 'Gulab Jamun', 'Dessert', 3.00);

Each row is like an ingredient added to the kitchen—real data now lives inside the Menu table.

Chapter 3: Mistakes & Fixes

Oops! Suvan entered a duplicate DishID.

The system throws an error:

“Primary key violation.”

He learns:

• Every DishID must be unique.
• SQL protects your data from accidental duplication.

Chapter 4: Looking Inside

Suvan checks if his menu looks good:

SELECT * FROM Menu;

It returns:

1 | Masala Dosa   | Main Course | 7.99  
2 | Filter Coffee | Beverage    | 2.50  
3 | Gulab Jamun   | Dessert     | 3.00

He smiles—it’s working.

Chapter 5: Bulk Insert – Getting Ready for Launch

On launch day, he adds more dishes with one query:

INSERT INTO Menu (DishID, DishName, Category, Price)
VALUES 
(4, 'Idli Vada', 'Main Course', 5.50),
(5, 'Lassi', 'Beverage', 3.25),
(6, 'Rasgulla', 'Dessert', 3.10);

No need for one-by-one entry—bulk insert saves time.

Lessons Suvan Learned:

1. Use CREATE TABLE to build the foundation.
2. Use INSERT INTO to feed data into your table.
3. Set a primary key to avoid duplicates.
4. Insert multiple rows at once for speed.
5. Always SELECT * to verify your data.

Conclusion: Your Table Is Your Café

In Suvan’s story, the SQL table is like a kitchen shelf:

• Columns define what to expect (name, type, price)
• Rows are actual dishes added day by day
• SQL helps keep everything organized, clean, and easy to update

Meet Ava, a data analyst for a fitness app. Her job is to help teams understand when users sign up, complete workouts, or cancel their plans. But there’s one tricky character she deals with every day: Time.

Let’s follow her journey as she learns how to tame dates and times in SQL.

Chapter 1: The Clock Ticks – Why Date/Time Matters

One day, Ava was asked:

“How many users signed up last month?”

She opened the database. The SignupDate column looked like this:

2025-03-01 08:12:45  
2025-03-22 17:33:00  
2025-04-01 09:01:15

At first glance, it was just a list. But Ava knew each timestamp held meaning:

• The date told her when something happened.
• The time helped her understand user habits—morning vs. night logins.
• Together, they formed a timeline of events.

Without handling these properly, her results would be inaccurate, confusing, or misleading.

Chapter 2: Querying the Clock – Using WHERE with Dates

To find signups in March 2025, she wrote:

SELECT * FROM Users
WHERE SignupDate BETWEEN '2025-03-01' AND '2025-03-31 23:59:59';

This gave her every record between the start and end of March.

She also learned she could use functions like:

WHERE MONTH(SignupDate) = 3 AND YEAR(SignupDate) = 2025

But she realized: performance is better when using BETWEEN with exact ranges, because it’s easier to index.

Chapter 3: Trimming the Time – When Only Dates Matter

Sometimes, her teammates only cared about dates, not exact times. So she used:

SELECT CAST(SignupDate AS DATE) AS SignupDay

or in SQL Server:

CONVERT(DATE, SignupDate)

This helped her group users by date:

SELECT CONVERT(DATE, SignupDate) AS SignupDay, COUNT(*)
FROM Users
GROUP BY CONVERT(DATE, SignupDate);

Chapter 4: Time Zones – The Invisible Trap

Ava later noticed that users in California signed up at “midnight”, but the system said it was “8 AM”.

Why? The database stored everything in UTC!

So she learned to:

• Convert to local time zones in queries or app logic
• Store timestamps in UTC for consistency
• Use AT TIME ZONE in SQL Server or adjust manually in others

Chapter 5: Comparing Across Time – INTERVAL and DATEDIFF

Her manager asked:

“How many users haven’t logged in for 30 days?”

Ava used:

SELECT * FROM Users
WHERE DATEDIFF(DAY, LastLoginDate, GETDATE()) > 30;

She also used DATEADD() to shift time:

WHERE LastLoginDate < DATEADD(DAY, -30, GETDATE());

Now she could track churn, re-engagement, and seasonal activity with confidence.

Chapter 6: The Clean-Up – Best Practices Ava Learned

1. Always store time in UTC; convert only for display.
2. Use date functions carefully—some are slow if misused.
3. Index date columns if used frequently in filters.
4. Store DATE and DATETIME with intent:
   
   • DATE when time isn’t needed
   • DATETIME for tracking moments/events
6. Handle null dates wisely:

WHERE LastLoginDate IS NOT NULL

Conclusion: Time Is Data’s Best Friend—If You Respect It

Dates and times are more than just numbers—they tell stories, show trends, and help drive decisions.

Thanks to SQL and a few smart functions, Ava became the Timekeeper of her company—cleaning, filtering, and analyzing moments that matter.

Imagine SQL as a friendly restaurant host named Sam. Every day, he helps you pick exactly what you want from the big menu of data.

Let’s follow Sam through a day in his life, guiding customers through a query.

1. SELECT – What Do You Want to See?

Sam (the host):

“Welcome! What do you want to see today? The whole menu? Or just the desserts?”

SELECT * FROM Menu;
-- or
SELECT DishName, Price FROM Menu;

Why Use It?

Use SELECT * when exploring all columns (for learning/debugging).

Use specific columns (SELECT DishName, Price) when building reports or dashboards.

2. FROM – Where Is It Coming From?

Customer:

“Show me the items… but from the Dessert Menu, not the main course.”

FROM DessertMenu

Why Use It?

FROM tells SQL which table to pull data from—just like choosing a menu section in a restaurant.

3. WHERE – Filtering the List

Sam:

“Got it! Now, do you want to see everything, or only dishes under $10?”

WHERE Price < 10

Why Use It?

The WHERE clause filters data to show only what meets your condition, like selecting vegetarian dishes or orders from a specific city.

4. GROUP BY – Summarizing Similar Things

Sam:

“Would you like to know how many items we have in each category?”

SELECT Category, COUNT(*) FROM Menu GROUP BY Category;

Why Use It?

GROUP BY is used when you want to summarize data (like totals, counts, averages) for each group—like how many desserts, how many starters.

5. ORDER BY – Sort It Your Way

Sam:

“Should I list them by price? Or alphabetically?”

ORDER BY Price ASC

Why Use It?

ORDER BY sorts your results—by name, date, sales, or any column—ascending or descending.

Let’s Put It All Together

Here’s how Sam handles a full customer request:

Customer:

“I want to see the name and price of all desserts under $10, grouped by category, and sorted from cheapest to most expensive.”

Sam (in SQL):

SELECT DishName, Price
FROM DessertMenu
WHERE Price < 10
GROUP BY DishName, Price
ORDER BY Price ASC;

Conclusion:

Each part of a SQL query plays a role—like team members at a restaurant:

• SELECT – what’s on the plate
• FROM – which kitchen it’s coming from
• WHERE – picky customer requests
• GROUP BY – organized report for the chef
• ORDER BY – plating it neatly

With just a few words, you can turn a mountain of data into a made-to-order report.

In the business world, numbers tell the truth—but only if they’re clean, consistent, and well-prepared. That’s where SQL numeric functions come to the rescue.

Whether you’re dealing with messy discounts, rounding errors, or unknown values, these functions work behind the scenes like quiet superheroes, cleaning and calculating your way to accurate reports.

Let’s meet the squad:

1. ROUND() – The Smoother

Story:

You’re preparing a billing report, and the prices are showing up like 99.9999. It looks unprofessional.

Fix:

SELECT ROUND(Price, 2) FROM Products;

Result:

$99.9999 becomes $100.00. Clean, tidy, and presentable.

2. CEILING() and FLOOR() – The Round-Up & Round-Down Twins

Story:

You’re calculating shipping estimates. You don’t want to undercharge or overpromise.

Fix:

SELECT CEILING(Weight) AS ShipWeight FROM Packages;
SELECT FLOOR(Discount) AS AdjustedDiscount FROM Sales;

Result:

Always fair—and aligned with business logic.

3. ABS() – The Peacemaker

Story:

Refunds are showing up as -50, but your finance team wants to see them as positive values.

Fix:

SELECT ABS(RefundAmount) AS CleanRefund FROM Transactions;

Result:

No more confusion over signs. Just clean numbers.

4. POWER() – The Calculator

Story:

You’re calculating compound interest or growth projections. You need exponential power.

Fix:

SELECT POWER(1.05, 5) AS FiveYearGrowth;

Result:

Growth made easy and accurate.

5. MOD() – The Validator

Story:

You want to find even or odd transactions, or segment records into batches.

Fix:

SELECT TransactionID
FROM Orders
WHERE MOD(TransactionID, 2) = 0;

Result:

Only even-numbered records are returned.

6. ISNULL() or COALESCE() – The Fallback Hero

Story:

Your price column has missing values, and it’s breaking your calculations.

Fix:

SELECT ISNULL(Discount, 0) FROM Sales;
-- or
SELECT COALESCE(Discount, 0) FROM Sales;

Result:

No more blanks—just safe defaults.

7. AVG(), SUM(), MIN(), MAX() – The Analysts

Story:

You want to know your top-performing product, average order size, or lowest shipping fee.

Fix:

SELECT AVG(OrderAmount), MAX(OrderAmount), MIN(OrderAmount)
FROM Orders;

Result:

Instant insights with just one line of SQL.

Conclusion:

Just like string functions tidy up your words, numeric functions are the daily superheroes keeping your numbers sharp, readable, and reliable.

They don’t just compute—they clean, standardize, and make your data meaningful.