{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"Ingest Spark DataFrames into Amazon SageMaker FeatureStore's online and offline stores, and load feature definitions from schema.","skillfed_tags":["aws-sagemaker","spark-connector","feature-engineering"],"use_cases":["Ingest training data from Spark DataFrames into SageMaker FeatureStore offline store to reduce WCU costs.","Load feature definitions from a Spark DataFrame schema for use with the SageMaker CreateFeatureGroup API.","Retrieve and debug records that failed during streaming ingestion to FeatureStore.","Build feature pipelines on EMR clusters that feed data directly into SageMaker FeatureStore.","Access offline store data via Lake Formation when registered with AWS Lake Formation (PySpark 3.5+)."],"what_it_does":"This is a PySpark connector for Amazon SageMaker FeatureStore that lets you ingest data from Spark DataFrames directly into FeatureStore's online and offline stores. It provides a main interface for operations like data ingestion, feature definition loading from schema, and retrieval of failed ingestion records. The package bundles pre-built JARs for Spark versions 3.1 through 3.5 and automatically selects the correct one based on your installed PySpark version.\n\nThe connector is designed for use in Spark environments, including AWS EMR clusters and SageMaker Notebook instances. It supports optional Lake Formation credentials for cross-account access when the offline store is registered with AWS Lake Formation (PySpark 3.5+ only). The package requires PySpark and NumPy to be pre-installed, and compatibility between Python and PySpark versions is constrained by a documented matrix.","worth_installing":"Yes. Low install friction, active maintenance, permissive Apache-2.0 license, no known vulnerabilities, and a focused feature set for SageMaker FeatureStore integration. Install if you need to ingest Spark DataFrames into FeatureStore; verify Python/PySpark version compatibility against the documented matrix before proceeding."},"id":"sagemaker-feature-store-pyspark","links":{"html":"https://skillfed.io/packages/sagemaker-feature-store-pyspark","md":"https://skillfed.io/packages/sagemaker-feature-store-pyspark.md","pypi":"https://pypi.org/project/sagemaker-feature-store-pyspark/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-29","license_spdx":null,"license_treatment":"permissive","name":"sagemaker-feature-store-pyspark","python_support":"supports_current","summary":"Amazon SageMaker FeatureStore PySpark Bindings"},"popularity":{"monthly_downloads":159902,"position":10683,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.0.0"}