Stream Feature View

Stream Feature Views compute feature values from a continuous streaming data source. They support the near real-time calculation of features with a freshness of less than 1s.

Stream Feature Views also support point-in-time correct training data generation, as well as backfills of newly created features, from a historical offline log of event data.

Tecton offers two different compute engines for Streaming Features:

• Rift: records sent to the Stream Ingest API are optionally transformed with Rift and then written directly to the Feature Store. The Stream Ingest API is the right choice if you prefer the simple experience of Python-based transformation environments, want to ingest pre-computed features, are building an event-driven architecture, or simply if you need very fresh features and every millisecond counts.
• Spark Structured Streaming: records are read from your streaming source, transformed, and written to the Feature Store by a Spark Structured Streaming job in your data plane. Spark Streaming features may be the right choice if your existing data stack already heavily relies on Spark and Spark Structured Streaming.