StreamFeatureView is a type of feature view in Feast that allows you to define features that are continuously updated from a streaming source. It is designed to handle real-time data ingestion and feature generation, making it suitable for use cases where features need to be updated frequently as new data arrives.

Supported Compute Engines

• LocalComputeEngine
• SparkComputeEngine
• FlinkComputeEngine

Key Capabilities

• Real-time Feature Generation: Supports defining features that are continuously updated from a streaming source.

• Transformations: Apply transformation logic (e.g., feature_transformation or udf) to raw data source.

• Aggregations: Define time-windowed aggregations (e.g., sum, avg) over event-timestamped data.

• ⚡ Tiling with Intermediate Representations: Enable efficient pre-aggregation with correct merging semantics for holistic aggregations like avg and std. This provides faster queries while maintaining mathematical accuracy. Learn more about tiling

• Feature resolution & execution: Automatically resolves and executes dependent views during materialization or retrieval.