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Yes, variations of cumulative aggregation and window function problems are common in technical interviews at large tech companies. They test a candidate’s understanding of SQL analytics, ordering, and partitioning concepts.
The optimal approach is to use a SQL window function to compute a running total of games played. By partitioning rows by player_id and ordering by event_date, you can efficiently calculate the cumulative games played for each player over time.
Window functions enable operations like running totals, rankings, and moving averages without collapsing rows through aggregation. This makes them perfect when you need per-row results along with aggregated insights.
Window functions are the most suitable SQL feature for this problem. They allow you to perform calculations across related rows while preserving each row’s original context, making them ideal for cumulative metrics.