The Running Total Pattern in Data Engineering

Concepts: pyRunningTotal, pyCumsum, pyDataMetrics

Prefix sums have a second life in data engineering that is completely separate from range queries: running totals. Cumulative revenue. Running user count. Rolling metric baselines. Whenever you need 'what is the total so far at each point in time,' you are computing a prefix sum. In pandas, this is cumsum(). In SQL, it is SUM() OVER (ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW). In Python, it is the prefix array you just built. All three are computing the exact same thing. Knowing this equivalence makes you dangerous in interviews because you can connect algorithmic thinking to production tools. Manual Prefix Sum vs pandas cumsum The point is not that you should use a manual prefix array instead of pandas cumsum in production -- you should absolutely use pandas. The poin