Generating accurate training data for real-time features is notoriously difficult, often requiring duplicate logic and introducing the risk of train-serve skew. With Apache Hamilton and Narwhals, teams can define feature transformations once and execute them seamlessly across both real-time and batch environments. This unified approach supports low-latency inference, scalable backfills, and consistent feature definitions—streamlining development and enhancing reliability in high-throughput, real-time AI systems.