sagemaker-feature-store-pyspark-3-1

import os from pyspark.sql import SparkSession from sagemaker_featurestore_pyspark import FeatureStoreManager # IMPORTANT: For local PySpark execution, you MUST include the spark.jars.packages config. # Replace '1.1.3' with the exact version of the sagemaker-featurestore-pyspark-sdk you are using. # Ensure PySpark and Java are installed and configured for your environment. spark = SparkSession.builder \ .appName("FeatureStorePySparkQuickstart") \ .config("spark.jars.packages", "software.amazon.sagemaker:sagemaker-featurestore-pyspark-sdk:1.1.3") \ .getOrCreate() # Replace with your actual Feature Group name and AWS region feature_group_name = os.environ.get('SAGEMAKER_FEATURE_GROUP_NAME', 'your-feature-group-name') aws_region = os.environ.get('AWS_REGION', 'us-east-1') # Initialize FeatureStoreManager # AWS credentials are typically sourced from the Spark environment (IAM Role, AWS_ACCESS_KEY_ID/SECRET_ACCESS_KEY). fs_manager = FeatureStoreManager(spark_session=spark, region=aws_region) try: # Read data from the Feature Group's online store df = fs_manager.read_feature_group( feature_group_name=feature_group_name ) print(f"Successfully read data from Feature Group: {feature_group_name}") df.show(5) df.printSchema() except Exception as e: print(f"Error interacting with Feature Group {feature_group_name}: {e}") print("Troubleshooting: Ensure Feature Group exists, credentials are set, and Spark environment is configured (esp. 'spark.jars.packages').") finally: spark.stop()