Real-Time AI Feature Engineering with Spark Structured Streaming and Databricks Feature Store

Short summary

Databricks Feature Store solves training-serving skew by storing feature logic alongside data and enforcing point-in-time lookups. Use Spark Structured Streaming to read Kafka events, compute windowed aggregations, and write continuously via foreachBatch. Tutorial covers architecture, environment setup, streaming pipeline, point-in-time correct training datasets, and online feature serving.

• •Databricks Feature Store eliminates training-serving skew through unified batch/online computation
• •Spark Structured Streaming with stateful window aggregations powers millisecond-latency feature updates
• •Point-in-time lookups prevent data leakage and ensure model reproducibility across versions

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