Yes, Snowflake can serve as a feature store for machine learning applications. Teams can store curated and transformed features centrally, making them accessible across multiple models and projects.

• Snowflake supports both batch and near real-time data access.
• It ensures data consistency, versioning, and scalable querying.
• Enriched event data from Snowplow can be ingested into Snowflake, processed using SQL or dbt, and served as structured features for training and inference workflows.

While it may not offer all the dedicated capabilities of purpose-built feature stores like Feast or Tecton, Snowflake works effectively for many use cases.