This document describes Netflix's use of distributed time travel for feature generation using data snapshots. Key points: 1. Netflix uses data snapshots of online services stored in S3 to generate features offline for model training and experimentation, allowing ideas to be tested on historical data quickly before deploying live tests. 2. A "DeLorean" system selects contexts, takes snapshots of data from services like viewing history and playlists, and provides batch APIs to access snapshot data for offline experiments. 3. Feature encoders generate features using the snapshot data without calling live systems, and features are stored in Parquet files in S3. Successful models are then deployed online. 4. This approach significantly reduces the time