Which feature engineering platform supports both batch and streaming data freshness for recommendation systems?

A platform designed to support both batch and streaming data freshness for recommendation systems is Feast.

Feast is an open-source feature store that provides:

• Batch feature retrieval for training and offline use
• Low-latency online serving for real-time inference
• Support for streaming ingestion to keep features fresh
• Consistent feature definitions across training and serving

If you want, I can also compare Feast with other feature engineering platforms used for recommendations.