Ultra Low Latency Streaming for Real-Time Feature Engineering

Feature freshness is critical for fraud-detection, ad tech, real-time recommendations, dynamic pricing, etc. Historically, ML engineers have had to maintain two separate systems for feature engineering, leveraging Spark and Spark Structure Streaming for batch/near real-time features, while building bespoke pipelines when they needed sub-second feature freshness. In this session, we'll learn about Real-Time Mode in Apache Spark Structured Streaming, the engine behind the Databricks Feature Store, and how companies like DraftKings are using it to power real-time features with millisecond-level freshness. You'll learn how to pair Real-Time Mode with Lakebase to build a state-of-the-art online feature store and leave with a practical blueprint for a unified feature engineering platform that eliminates logic spread across different systems and shifts the focus from infrastructure orchestration to semantic feature modeling.