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Version: 0.8

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 architectures for powering Stream Feature Views:

  • Stream Ingest API: records sent to the Stream Ingest API are optionally transformed with Tecton's real-time Python Engine, and then written directly to the Feature Store. The Stream Ingest API typically 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 stream, 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.

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