Nate Rosidi Publishes 4 SQL Window-Function Patterns for Business Problems

1 articles · Updated · KDnuggets · May 20

Four advanced SQL window-function patterns anchor Nate Rosidi’s new article, which moves beyond basics to show how analysts can solve practical business problems in a single query flow.
The piece focuses on running totals, gaps-and-islands sessionization, cohort analysis and percentile-based ranking, pairing each pattern with interview-style examples from Amazon, LinkedIn and Meta, DoorDash, and Google and Netflix.
Rosidi explains the mechanics through functions such as SUM() OVER(), LAG(), FIRST_VALUE() and PERCENTILE_CONT(0.95), showing how chained CTEs and window frames preserve row-level detail while adding cumulative or comparative metrics.
The article’s broader message is that window functions are most powerful when composed to handle retention, revenue tracking, user streaks and fraud detection directly in the database rather than through separate processing steps.

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