retention

The canonical product-analytics SQL question. The signal is grouping users by signup cohort, computing each activity's period offset, and counting distinct returners per offset to build the retention triangle.

Metrics, traces, and IoT data are append-heavy, time-ordered, and rarely updated. A general database handles this badly. The wins come from columnar layout, compression tuned for timestamps, downsampling, and retention. Here is the time-series design interviewers want.

Saving a churning account is diagnosis under pressure: find the real reason, fix what you can, and earn back trust with action. Interviewers want a method, not a discount. Here is the move.