window-function-builder

To calculate a 7-day moving average using SQL window functions, use the `AVG() OVER()` syntax with a frame specification like `ROWS BETWEEN 6 PRECEDING AND CURRENT ROW`, ordering by date. This provides a rolling average for time-series analytics.

For cohort analysis and customer lifecycle tracking, use SQL window functions like `ROW_NUMBER` for ranking, `LAG` for tracking days between orders, and `SUM() OVER()` for calculating lifetime value. These functions help identify repeat versus new customers.

Yes, the SQL window function guidance covers platform-specific features for PostgreSQL, SQL Server, BigQuery, and Snowflake. It provides syntax, frame specifications, and performance optimization tips tailored to each database platform.

Ranking window functions sort rows, aggregate window functions compute values across sets, offset functions like `LAG` access prior rows, and statistical functions perform calculations. All four are supported with detailed syntax and business analytics patterns.

To optimize SQL window function performance, apply best practices and frame specifications to limit data scanned. The Skill provides performance optimization tips for analytical queries, ensuring efficient execution across supported database platforms.

Yes, you can calculate customer lifetime value from order history using `SUM() OVER()` to aggregate totals, `LAG` to analyze days between orders, and `ROW_NUMBER` to distinguish repeat customers. This enables deep business analytics.