{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"A PySpark connector for Amazon SageMaker FeatureStore that ingests data from Spark DataFrames into FeatureStore's online and offline stores, with automatic feature definition loading.","skillfed_tags":["aws-sagemaker","spark-connector","feature-engineering"],"use_cases":["Batch ingest feature data from a Spark cluster into SageMaker FeatureStore's online and offline stores for ML model training.","Automatically load and register feature definitions from Spark DataFrames to create SageMaker feature groups without manual schema definition.","Integrate Spark-based ETL pipelines with SageMaker's feature management infrastructure for centralized feature governance.","Populate both online (low-latency) and offline (batch) feature stores from a single Spark job to keep training and serving features in sync."],"what_it_does":"This package is a PySpark connector that bridges Apache Spark and Amazon SageMaker FeatureStore, enabling you to push feature data from Spark DataFrames directly into SageMaker's online and offline feature stores. It handles the translation between Spark's distributed data model and SageMaker's feature ingestion API, and includes utilities to automatically load feature definitions to simplify feature group creation.\n\nThe connector is designed for batch ingestion workflows where you have feature data in Spark and need to materialize it into SageMaker FeatureStore for model training or serving. It abstracts away the details of the SageMaker API calls, letting you work primarily with familiar Spark DataFrame operations.","worth_installing":"Yes, if you are already running Spark 3.3 workloads on AWS and need to ingest features into SageMaker FeatureStore\u2014it eliminates manual API calls and simplifies the integration. However, proceed with caution: maintenance is dormant (554 days since last release), so compatibility with recent SageMaker or Spark updates is uncertain. Verify compatibility with your SageMaker API version before adopting in production."},"id":"sagemaker-feature-store-pyspark-3-3","links":{"html":"https://skillfed.io/packages/sagemaker-feature-store-pyspark-3-3","md":"https://skillfed.io/packages/sagemaker-feature-store-pyspark-3-3.md","pypi":"https://pypi.org/project/sagemaker-feature-store-pyspark-3-3/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2025-02-06","license_spdx":null,"license_treatment":"permissive","name":"sagemaker-feature-store-pyspark-3.3","python_support":"unspecified","summary":"Amazon SageMaker FeatureStore PySpark Bindings"},"popularity":{"monthly_downloads":100317,"position":12993,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.3"}