Feature Store Freshness

This post details how the Databricks Feature Store achieves sub-second freshness for serving features. It likely covers architectural improvements, data pipeline optimizations, and caching strategies to ensure low-latency feature availability for real-time ML applications. This builds upon the core functionality of the Feature Store by focusing on a critical performance metric.