2 Repos
Applies user-defined transformations to streaming data through a pluggable interface.
Distinct from Streaming Processors: Distinct from Streaming Processors: focuses on the pluggable interface for custom processors, not the processing engine itself.
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Enterprise job scheduling middleware with distributed computing ability.
Ships a pluggable processor framework supporting Java, Shell, Python, and HTTP-based task logic.
Feast is an open-source feature store for machine learning that provides a central platform for defining, storing, and serving features across both training and inference workflows. It operates as a declarative system where feature definitions are written as code in Python files, synchronized to a central registry, and made available for low-latency online retrieval or point-in-time correct historical joins for training datasets. The project abstracts storage behind a pluggable architecture, allowing offline and online backends to be swapped without changing retrieval logic, and coordinates ma
Applies user-defined transformations to streaming data through a pluggable Stream Processor interface.