SQL Practice Problems - Day 14
Focus: Advanced Grouping and Pivoting Operations

1 - Generate sales totals by category, by month, and overall total using GROUPING SETS.
Input Tables: Sales_Transactions, Products
Output: Category, Month, Total_Sales, Grouping_Level

2 - Create all possible combinations of sales aggregates by category, size, and color using CUBE.
Input Tables: Sales_Transactions, Products
Output: Category, Size, Color, Total_Sales

3 - Generate hierarchical sales aggregates by year, quarter, and month using ROLLUP.
Input Table: Sales_Transactions
Output: Year, Quarter, Month, Total_Sales

4 - Pivot monthly sales data to show categories as columns and months as rows.
Input Tables: Sales_Transactions, Products
Output: Month, Category1_Sales, Category2_Sales, Category3_Sales

5 - Unpivot wide inventory table with separate columns for each warehouse location.
Input Table: Inventory_Wide
Output: SKU_Code, Warehouse, Stock_Qty

6 - Use GROUPING SETS to show sales by category, by customer region, and combined.
Input Tables: Sales_Transactions, Products, Customers
Output: Category, Region, Total_Sales, Grouping_ID

7 - Create a CUBE operation for sales analysis by product category, size, and customer tier.
Input Tables: Sales_Transactions, Products, Customers
Output: Category, Size, Customer_Tier, Total_Sales

8 - Generate ROLLUP report for sales hierarchy: year → quarter → month → day.
Input Table: Sales_Transactions
Output: Year, Quarter, Month, Day, Total_Sales

9 - Pivot sales data to show monthly totals for each product category as separate columns.
Input Tables: Sales_Transactions, Products
Output: Year, Jan_Sales, Feb_Sales, Mar_Sales, ...

10 - Unpivot pricing table with different platform prices into normalized format.
Input Table: Pricing_Wide
Output: SKU_Code, Platform, Price

11 - Use GROUPING SETS to analyze sales by multiple dimensions with subtotals.
Input Tables: Sales_Transactions, Products, Customers
Output: Category, Size, Region, Total_Sales, Grouping_Text

12 - Create CUBE for comprehensive sales analysis across all dimension combinations.
Input Tables: Sales_Transactions, Products, Customers
Output: Category, Size, Color, Region, Total_Sales

13 - Generate ROLLUP for geographic hierarchy: country → region → city sales totals.
Input Tables: Sales_Transactions, Customers
Output: Country, Region, City, Total_Sales

14 - Pivot customer sales data to show spending by product category columns.
Input Tables: Sales_Transactions, Products, Customers
Output: Customer_ID, Category1_Spend, Category2_Spend, ...

15 - Unpivot seasonal sales data with quarters as columns into long format.
Input Table: Seasonal_Sales_Wide
Output: SKU_Code, Quarter, Sales_Amount