{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"Connects Apache Spark DataFrames to Amazon SageMaker FeatureStore for ingesting feature data into online and offline stores, with automatic feature definition loading.","skillfed_tags":["aws-integration","spark-connector","feature-engineering"],"use_cases":["Batch ingest computed features from a Spark DataFrame into SageMaker FeatureStore for ML model training.","Load feature definitions automatically from Spark schema to reduce boilerplate when creating new FeatureGroups.","Synchronize features computed in a Spark pipeline with SageMaker's online store for real-time inference.","Populate both online and offline stores in a single operation from a Spark job."],"what_it_does":"This package is a Spark connector that bridges Apache Spark DataFrames and Amazon SageMaker FeatureStore. It allows you to ingest feature data directly from Spark into both the online and offline stores of a FeatureGroup, and includes utilities to automatically load feature definitions to streamline FeatureGroup creation.\n\nThe connector is designed for teams already running Spark workloads on AWS who want to feed computed features into SageMaker's managed feature store. It requires an existing Spark cluster and SageMaker FeatureStore setup with proper AWS credentials; it is not a standalone tool but rather a bridge between two AWS services.","worth_installing":"Yes, if you are already running Spark on AWS and have an active SageMaker FeatureStore deployment. No, if you are looking for a general-purpose feature store or do not have Spark infrastructure in place. Caution: the package is dormant (554 days since last release); verify with AWS documentation that it remains supported for your Spark and SageMaker versions before adopting in production."},"id":"sagemaker-feature-store-pyspark-3-1","links":{"html":"https://skillfed.io/packages/sagemaker-feature-store-pyspark-3-1","md":"https://skillfed.io/packages/sagemaker-feature-store-pyspark-3-1.md","pypi":"https://pypi.org/project/sagemaker-feature-store-pyspark-3-1/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2025-02-06","license_spdx":null,"license_treatment":"permissive","name":"sagemaker-feature-store-pyspark-3.1","python_support":"unspecified","summary":"Amazon SageMaker FeatureStore PySpark Bindings"},"popularity":{"monthly_downloads":378658,"position":7117,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.3"}