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- Add LakeFormationConfig class to configure Lake Formation governance on offline stores - Implement FeatureGroup subclass with Lake Formation integration capabilities - Add helper methods for S3 URI/ARN conversion and Lake Formation role management - Add S3 deny policy generation for Lake Formation access control - Implement Lake Formation resource registration and S3 bucket policy setup - Add integration tests for Lake Formation feature store workflows - Add unit tests for Lake Formation configuration and policy generation - Update feature_store module exports to include FeatureGroup and LakeFormationConfig - Update API documentation to include Feature Store section in sagemaker_mlops.rst - Enable fine-grained access control for feature store offline stores using AWS Lake Formation
Replace 10 bare print() calls with a single logger.info() call for the S3 deny policy output in enable_lake_formation(). This makes the policy display consistent with the rest of the LF workflow which uses logger. Update 12 tests to mock the logger instead of builtins.print. --- X-AI-Prompt: replace print with logger.info for s3 bucket policy display in enable_lake_formation X-AI-Tool: kiro-cli
Rename the mlops FeatureGroup class to FeatureGroupManager to distinguish it from the core FeatureGroup base class. Update all references in unit and integration lake formation tests. Fix missing comma in __init__.py __all__ list. --- X-AI-Prompt: rename FeatureGroup to FeatureGroupManager and update lakeformation tests X-AI-Tool: kiro-cli
…ate, update, get functions
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Description of changes:
NOTE: Based off of BassemHalim:feature-store-lakeformation @ commit d21ca67ab723cf5fcef9e6e1090efcd643e1ded3
Design
We will not be making any changes to the sagemaker core package as this code is autogenerated based off Feature Store APIs. This means it will be overwritten if we are not careful with maintenance. We will be making all our changes in the mlops package instead. In here we will be making a new class FeatureGroupManager that will extend the FeatureGroup class from the sagemaker core package. In the extended class we will create a new input type called IcebergProperties and overload 3 core functions and create 2 new helper functions.
IcebergProperties type
This new type takes in a wrapper for a Dict[str, str] that also includes some validation of the keys to make sure they are a part of our validated list.
Overloaded functions
Helper functions
Security considerations
Usage
Create FG with Iceberg Properties
Update existing FG with Iceberg Properties
Get a FG's icebergProperties