Data Analytics on Telegram: “⚠️ Every additional join can change the number o…”

⚠️ Every additional join can change the number of rows.

• Always check whether the resulting grain is still what you expect.

🔹 16. The Most Important Analytical Pattern

Many advanced SQL problems can be solved using this structure:

•

1. Filter the raw data → 2. Join required tables → 3. Aggregate to the correct grain → 4. Apply window functions → 5. Filter the analytical result → 6. Present the final output

For example:

• Orders → Filter → GROUP BY Customer → Calculate Sales → RANK() → Keep Top 3 → Final Report

• Learning this thought process is more valuable than memorizing individual queries.

💼 Real-World Business Problems You Should Practice

Sales

✅ Top 5 products by revenue

✅ Top products within each category

✅ Month with highest sales

✅ Revenue growth by month

✅ Customers contributing most revenue

Customers

✅ Customers with no orders

✅ Customers with declining purchases

✅ Most recent purchase per customer

✅ Repeat customers

✅ Average order value per customer

Operations

✅ Orders taking longer than expected

✅ Products never sold

✅ Duplicate transactions

✅ Most active regions

✅ Employees with above-average performance

🎯 SQL Interview Challenge

Question: Find the highest-selling product in each category.

Think about the problem before writing the query:

• Product → Category → Total Sales → Rank Within Category → Keep Rank 1

A possible solution:

WITH Product_Sales AS (
    SELECT Product_ID, Category, SUM(Sales) AS Total_Sales
    FROM Product_Sales_Data
    GROUP BY Product_ID, Category
),
Ranked_Products AS (
    SELECT Product_ID, Category, Total_Sales,
           RANK() OVER (PARTITION BY Category ORDER BY Total_Sales DESC) AS Sales_Rank
    FROM Product_Sales
)
SELECT Product_ID, Category, Total_Sales FROM Ranked_Products WHERE Sales_Rank = 1;

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