databricks-feature-engineering

import os from databricks.feature_engineering import FeatureEngineeringClient # For local execution outside a Databricks notebook, ensure these # environment variables are set for authentication. # os.environ['DATABRICKS_HOST'] = os.environ.get('DATABRICKS_HOST', 'https://<your-databricks-instance>.cloud.databricks.com') # os.environ['DATABRICKS_TOKEN'] = os.environ.get('DATABRICKS_TOKEN', 'dapi...') try: # The client automatically picks up credentials from the Databricks environment # or DATABRICKS_HOST/DATABRICKS_TOKEN environment variables. fe_client = FeatureEngineeringClient() print(f"Successfully initialized Databricks Feature Engineering Client: {type(fe_client)}") print("\nNote: Most Feature Engineering operations (like creating, writing, or reading tables)") print("require an active SparkSession, which is typically available in a Databricks notebook") print("or when using Databricks Connect (ensure it's configured locally).") print("\nFor example, to create a feature table or write data, you would need a 'spark' object.") except Exception as e: print(f"Failed to initialize Databricks Feature Engineering Client: {e}") print("Please ensure you are running in a Databricks environment or have 'DATABRICKS_HOST' and 'DATABRICKS_TOKEN' environment variables set.")