Find the top 3 products by total revenue. Revenue is calculated as price * quantity sold. Return product name and total revenue, ordered by revenue descending.
This basic aggregation problem is fundamental to Shopify's merchant analytics. Every e-commerce business needs to know which products drive the most revenue to inform inventory, marketing, and pricing decisions. This question tests your ability to perform calculations within aggregations, use GROUP BY correctly, and apply LIMIT for top-N queries—essential skills for any data analyst role.
Core concepts: SUM() with calculated columns (price * quantity), GROUP BY for product-level aggregation, ORDER BY for sorting, and LIMIT for top-N selection. Understanding the order of operations (calculation happens before aggregation) and how to combine multiple aggregate functions is key.
Shopify merchants use similar queries to: generate best-seller reports for inventory planning, identify products to feature in marketing campaigns, calculate product-level profitability, optimize warehouse space allocation, power recommendation engines with popularity signals, and create automated reorder triggers for high-velocity items.
When tackling this Shopify problem, the key is to understand the grain of the result. Are you returning one row per user, or one row per category? Always start by identifying your unique join keys and consider if filtered aggregations (CASE WHEN) are more efficient than multiple subqueries.
Be careful with NULL values in your JOIN conditions or aggregate functions. In interview scenarios, datasets often include edge cases like zero-count categories or duplicate entries that can throw off a simple COUNT(*) if not handled with DISTINCT.
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