These patterns are the building blocks of modern data analytics, used by every major company for critical business decisions. Netflix uses top-N patterns to find trending content, gap detection for viewing session analysis, and cohort analysis for subscriber retention. Amazon employs these patterns for product recommendations (top sellers per category), inventory management (gap detection in stock levels), and customer lifetime value analysis. Goldman Sachs uses time-series patterns for trading algorithms, running calculations for risk management, and percentage analysis for portfolio allocation. Tesla applies these patterns for manufacturing quality control, performance benchmarking, and predictive maintenance. These aren't academic exercises - they're the exact patterns that power billion-dollar business decisions every day.

Netflix Content Recommendation Engine - Multi-Pattern Analytics

Netflix combines multiple window function patterns to power their recommendation engine, analyzing 200M+ users and 15K+ titles to deliver personalized content suggestions. Create a comprehensive content recommendation system that identifies trending content, analyzes user behavior patterns, and generates personalized suggestions using multiple analytical patterns.

-- Netflix multi-pattern recommendation engine

-- Pattern 1: Top-N per group (trending content)

WITH trending_content AS (

SELECT

content_id,

genre,

title,

total_watch_hours,

unique_viewers,

-- Top content per genre

RANK() OVER (

PARTITION BY genre

ORDER BY total_watch_hours DESC

) as genre_popularity_rank,

-- Overall trending rank

RANK() OVER (ORDER BY total_watch_hours DESC) as overall_rank

FROM content_analytics

WHERE analysis_date >= CURRENT_DATE - INTERVAL '7 days'

),

-- Pattern 2: User behavior analysis with gap detection

user_viewing_patterns AS (

SELECT

user_id,

content_id,

watch_date,

watch_duration_minutes,

-- Detect viewing gaps

LAG(watch_date) OVER (

PARTITION BY user_id

ORDER BY watch_date

) as prev_watch_date,

-- Calculate engagement score

AVG(watch_duration_minutes) OVER (

PARTITION BY user_id

ORDER BY watch_date

ROWS BETWEEN 4 PRECEDING AND CURRENT ROW

) as recent_engagement_avg