Transform Business Logic into SQL (Step-by-Step Guide)

Most people don’t struggle with SQL syntax…

They struggle with thinking in SQL.

You understand the problem.
You know JOIN, GROUP BY, WINDOW FUNCTIONS.
But when it’s time to solve a real business question — you get stuck.

This guide fixes that.

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The Real Problem

SQL interviews (and real jobs) don’t test syntax.
They test your ability to:

👉 Break down a business problem
👉 Translate it into logical steps
👉 Convert those steps into SQL

If you master this, SQL becomes easy.

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🧩 The 5-Step Translation Framework

Use this every time:

1. Understand the business question
2. Identify entities (tables)
3. Define metrics (aggregations / conditions)
4. Break into sub-problems
5. Translate each step into SQL

💡 Think of SQL as the last step, not the first.

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🟢 Example 1 (Intermediate)

📌 Problem

Find customers who placed more than 3 orders in the last 30 days
AND spent more than $500.

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Step-by-Step Thinking

1. Understand the question

We need frequent + high-value customers within a time window.

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2. Identify tables

We only need:
👉 orders

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3. Define metrics

• Number of orders → COUNT(*)
• Total spend → SUM(amount)
• Time filter → last 30 days

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4. Break into steps

• Filter recent orders
• Group by customer
• Apply conditions

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5. Translate to SQL

SELECT customer_id
FROM orders
WHERE order_date >= DATEADD(DAY, -30, GETDATE())
GROUP BY customer_id
HAVING COUNT(*) > 3
   AND SUM(amount) > 500;

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💡 Key Insight

• Use WHERE → to filter rows
• Use HAVING → to filter aggregated results

👉 This distinction is critical in interviews.

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🔴 Example 2 (Advanced – FAANG Level)

📌 Problem

Find the top 2 most purchased products per category
based on total revenue in the last 90 days.

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Step-by-Step Thinking

1. Understand the question

We need top-performing products within each category.

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2. Identify tables

• orders → dates
• order_items → quantity & price
• products → category

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3. Define metrics

• Revenue → quantity * price
• Total revenue → SUM()
• Ranking → RANK()

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4. Break into steps

• Join tables
• Filter last 90 days
• Aggregate revenue
• Rank within category
• Filter top 2

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5. Translate to SQL

WITH revenue_cte AS (
    SELECT 
        p.category_id,
        oi.product_id,
        SUM(oi.quantity * oi.price) AS total_revenue
    FROM orders o
    JOIN order_items oi ON o.order_id = oi.order_id
    JOIN products p ON p.product_id = oi.product_id
    WHERE o.order_date >= DATEADD(DAY, -90, GETDATE())
    GROUP BY p.category_id, oi.product_id
)
SELECT *
FROM (
    SELECT *,
           RANK() OVER (
               PARTITION BY category_id 
               ORDER BY total_revenue DESC
           ) AS rnk
    FROM revenue_cte
) t
WHERE rnk <= 2;

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💡 Key Insight

Whenever you hear:
👉 “Top N per group”

Think:
👉 Window Functions (RANK, ROW_NUMBER)

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🧪 Practice Problem (Try This)

📌 Problem

Find users who logged in 3 consecutive days.

How to Approach

• Use ROW_NUMBER()
• Create a grouping trick
• Identify consecutive sequences

👉 This is a classic FAANG interview question

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🎯 Final Takeaway

Stop memorizing SQL queries.
Start thinking in business logic → steps → SQL.

Because in the real world…
👉 Every SQL problem is just a business problem in disguise.

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