1 个仓库
Awesome GitHub RepositoriesStream Engine Configurations
Settings for defining compute engine parameters used in streaming materialization and retrieval tasks.
Distinct from Stream Computing Engines: Distinct from Stream Computing Engines: focuses on configuring the compute engine for streaming tasks rather than the engine itself.
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Awesome Stream Engine Configurations GitHub Repositories
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- feast-dev/feast
feast-dev/feast
6,727在 GitHub 上查看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
Allows configuring default or per-feature-view compute engine parameters for streaming materialization.
Pythonbig-datadata-engineeringdata-quality
