Data Analytics on Telegram: “This combines: GROUP BY SUM() CASE WHEN to creat…”

This combines:

GROUP BY

SUM()

CASE WHEN

to create a business-ready result.

2️⃣7️⃣ Important SQL Execution Concept

A simplified logical order of SQL processing is:

FROM

↓

WHERE

↓

GROUP BY

↓

HAVING

↓

SELECT

↓

ORDER BY

This helps explain why SQL behaves differently from how the query appears visually.

For example:

SELECT
    Region,
    SUM(Sales) AS Total_Sales
FROM Orders
GROUP BY Region
HAVING SUM(Sales) > 100000
ORDER BY Total_Sales DESC;

Think:

Get data → filter rows → group → calculate → filter groups → sort

Understanding SQL's logical processing order will become increasingly important as queries get more complex.

🧪 Practical Interview Challenge

Suppose you have:

Orders

Order_ID | Region | Sales | Profit

1001 | North | 120,000 | 20,000

1002 | South | 75,000 | 10,000

1003 | North | 30,000 | -5,000

1004 | West | 150,000 | 30,000

1005 | South | NULL | 8,000

Q1. Categorize orders by sales.

SELECT
    Order_ID,
    Sales,
    CASE
        WHEN Sales >= 100000 THEN 'High'
        WHEN Sales >= 50000 THEN 'Medium'
        ELSE 'Low'
    END AS Sales_Category
FROM Orders;

Q2. Find orders with missing sales.

SELECT *
FROM Orders
WHERE Sales IS NULL;

Q3. Replace missing sales with zero for display.

SELECT
    Order_ID,
    COALESCE(Sales, 0) AS Sales
FROM Orders;

Remember: this changes the display/calculation result, not necessarily the underlying data.

Q4. Categorize profitability.

SELECT
    Order_ID,
    CASE
        WHEN Profit > 0 THEN 'Profitable'
        WHEN Profit = 0 THEN 'Break-even'
        ELSE 'Loss'
    END AS Profit_Status
FROM Orders;

Q5. Count high-value orders.

SELECT
    COUNT(
        CASE
            WHEN Sales >= 100000 THEN 1
        END
    ) AS High_Value_Orders
FROM Orders;

Q6. Calculate profit margin safely.

SELECT
    Order_ID,
    Profit / NULLIF(Sales, 0) AS Profit_Margin
FROM Orders;

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