This API reference is for the Databricks feature engineering client.Feature Engineering Python API reference Was this article helpful?© Databricks 2025. All rights reserved. Apache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. Send us feedback | Priv...
fromdatabricks.feature_engineeringimport( FeatureFunction, FeatureLookup, FeatureEngineeringClient, ) fe = FeatureEngineeringClient() features = [# Lookup column `average_yearly_spend` and `country` from a table in UC by the input `user_id`.FeatureLookup( table_name="main.default.customer_profile...
from databricks.feature_engineering import FeatureLookup, FeatureFunction, FeatureEngineeringClient fe = FeatureEngineeringClient() features=[ FeatureLookup( table_name=feature_table_name, lookup_key="destination_id" ), FeatureFunction( udf_name=function_name, output_name="distance", input_bindings={ ...
Use the FeatureStoreClient.create_feature_table API:Python Copy fs = FeatureStoreClient(feature_store_uri=f'databricks://<scope>:<prefix>') fs.create_feature_table( name='recommender.customer_features', keys='customer_id', schema=customer_features_df.schema, description='Customer-keyed fea...
For better performance in point-in-time lookups, Databricks recommends that you apply Liquid Clustering (for databricks-feature-engineering 0.6.0 and above) or Z-Ordering (for databricks-feature-engineering 0.6.0 and below) on time series tables. Point-in-time lookup functionality is sometimes ref...
LlamaIndex is a data framework for your LLM applications - llama_index/CHANGELOG.md at feature/lindormsearch-vector-db · Rainy-GG/llama_index
Welcome, fellow integrators, to post number six in this blog post series , where I talk to SAP Cloud Integration practitioners, developers, architects and enthusiasts
Feature Engineering in Unity Catalog has a Python clientFeatureEngineeringClient. The class is available on PyPI with thedatabricks-feature-engineeringpackage and is pre-installed in Databricks Runtime 13.3 LTS ML and above. If you use a non-ML Databricks Runtime, you must install the client manu...
Next, create an instance of the Feature Store client. from databricks import feature_store fs = feature_store.FeatureStoreClient() To create a time series feature table, the DataFrame or schema must contain a column that you designate as the timestamp key. The timestamp key column must be ...
fromdatabricks.feature_engineeringimport( FeatureFunction, FeatureLookup, FeatureEngineeringClient, ) fe = FeatureEngineeringClient() features = [# Lookup column `average_yearly_spend` and `country` from a table in UC by the input `user_id`.FeatureLookup( table_name="main.default.customer_profile...