Sliding Windows in Data Pipelines

Concepts: pyRollingAgg, pyRateLimiter, pySessionWindow

Here is where you turn a coding answer into a data engineering answer. Sliding windows are not just LeetCode problems. They are the foundation of rolling aggregations, session analysis, anomaly detection, and rate limiting in production data systems. Every time you write AVG(revenue) OVER (ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) in SQL, you are using a fixed-width sliding window. When you use Flink's SlidingEventTimeWindows, you are using the same algorithm. Connecting the interview problem to these real systems is what makes the interviewer write 'strong DE judgment' on the scorecard. Rolling Aggregations The most direct application. Computing a 7-day rolling average of daily revenue. A 30-day rolling user count. A 1-hour rolling error rate. All are fixed-width sliding win