Analytical Patterns and Advanced SQL Queries

Key SQL Techniques

• Window Functions: Rolling aggregations, partitioned calculations
• GROUPING SETS: Multi-level aggregations in single queries
• CTEs: Modular query structure
• State Tracking: Growth accounting and cohort analysis

Web Analytics

File	Pattern	How Used	Why Useful
time-series-web-traffic-analysis.sql	Rolling Aggregations	Window functions with ROWS BETWEEN for cumulative sums	Track traffic trends over time with rolling metrics
user_activity_funnel_analysis.sql	User Journey Mapping	Self-joins to track page-to-page navigation	Understand user behavior and optimize conversion paths
event_to_conversion_analysis.sql	Conversion Funnel	CTEs with self-joins for same-day conversion tracking	Measure which pages drive conversions to payment
multi_dimensional_aggregation_pattern.sql	OLAP Reporting	GROUPING SETS for multi-level aggregations	Generate reports at different granularities in one query
dimensional_rollup_analysis.sql	Materialized Views	GROUPING SETS with automatic level identification	Create performance-optimized dashboard tables

User Growth & Retention

File	Pattern	How Used	Why Useful
growth_accounting.sql	State Change Tracking	CASE statements with FULL OUTER JOINs	Track user lifecycle states (New, Retained, Churned)
retention_analysis.sql	Cohort Analysis	Date arithmetic with GROUP BY	Measure user retention over time

Sports Analytics

File	Pattern	How Used	Why Useful
players/window_function_analysis.sql	Rolling Performance	Window functions with 90-game sliding windows	Analyze team/player performance trends over time
players/grouping_sets_analysis.sql	Multi-Dimensional Stats	GROUPING SETS for player/team/season aggregations	Efficient sports statistics reporting at multiple levels
players/change_state_tracking.sql	Career State Changes	FULL OUTER JOINs with CASE statements	Track player career transitions across seasons