Offline Feature Store

An offline feature store is the layer where training data and historical feature versions are stored. It is critical for model training, backtesting, feature analysis, and reproducibility. Unlike the online system, the main priority here is not ultra-low latency but historical correctness and volume management. A strong offline store enables experiments to be repeated consistently and preserves feature history reliably.