{"library":"sagemaker-feature-store-pyspark","title":"Amazon SageMaker Feature Store PySpark Bindings","description":"PySpark bindings for Amazon SageMaker Feature Store, enabling large-scale feature engineering and serving with Spark DataFrames. Current version 1.2.0, released monthly.","language":"python","status":"active","last_verified":"Mon Apr 27","install":{"commands":["pip install sagemaker-feature-store-pyspark"],"cli":null},"imports":["from sagemaker.feature_store.feature_store import FeatureStoreManager","from sagemaker_feature_store_pyspark import PySparkFeatureStore"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"from pyspark.sql import SparkSession\nfrom sagemaker.feature_store.feature_store import FeatureStoreManager\n\nspark = SparkSession.builder.getOrCreate()\nfs = FeatureStoreManager()\n\ndf = spark.createDataFrame([(1, 'a'), (2, 'b')], ['id', 'value'])\nrecord_id = 'id'\nfeature_group_name = 'my-feature-group'\nfs.ingest(df, feature_group_name, record_identifier_name=record_id)","lang":"python","description":"Creates a Spark DataFrame and ingests it into a SageMaker Feature Group using PySpark bindings.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}