Subqueries are everywhere in production SQL. Compare salaries to averages, filter customers who placed orders, calculate running totals, build complex reports. They're the building blocks for analytics, reporting, and business logic. Understanding subquery types helps you write clearer, faster queries.

E-commerce: Customer Segmentation

Marketing team needs to segment customers: high-value (>$1000 total), active (ordered in last 30 days), inactive (no orders in 90 days). Multiple subqueries for different segments.

E-commerce / Retail

Query time: 8 seconds to 400ms (20x faster). Marketing can segment 100K customers instantly. Derived table aggregates orders once, then joins to customers. Enables real-time customer segmentation for targeted campaigns. Correlated subqueries were executing 300K times (3 per customer). Derived table approach executes once.

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SaaS Platform: Feature Usage Analytics

Product team needs report showing users with their feature usage counts. Original query used scalar subqueries for each feature. Very slow with 50K users and 10 features.

SaaS / Technology

Query time: 45 seconds to 2 seconds (22x faster). Product team can analyze feature usage in real-time. Derived table with CASE pivot aggregates all features in one pass. Enables daily automated reports. Original approach was timing out in production.

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Financial Services: Account Risk Scoring

Risk team needs to score accounts based on transaction patterns. Multiple subqueries check different risk factors. Need to optimize for 1M accounts.

Banking / Finance

Query time: 5 minutes to 15 seconds (20x faster). Risk scoring can run hourly instead of daily. CTE pre-aggregates all transaction stats in one pass, then joins to accounts. Enables real-time risk monitoring. Critical for compliance and fraud detection.

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