Point-in-Time Correctness
A critical data engineering principle ensuring that historical training data only contains information that was actually knowable at the exact time of predic...
Think of It Like This
Like making sure you don't use tomorrow's newspaper to help you place a bet on today's horse race.
Failing to maintain point-in-time correctness causes insidious data leakage, where a model looks incredibly accurate in training but fails catastrophically in production. Feature stores handle this automatically through complex 'as-of' joins, guaranteeing that a prediction made on Tuesday only uses data fully recorded before Tuesday.