Build Real-Time ML Features with Spark Streaming and Databricks Feature Store

This article details how to build point-in-time correct feature pipelines using Spark Structured Streaming and Databricks Feature Store. It addresses training-serving skew, data leakage, and feature staleness by unifying offline and online feature computation. The approach ensures production-grade ML systems with low-latency serving.