Question

A data engineer maintains a feature table `main.retail.customer_features` in Unity Catalog. Each night, a job computes updated aggregate features (e.g., 30-day spend) for a subset of customers whose activity changed that day. Existing feature rows for customers with no activity must remain unchanged, and updated customers should have their existing rows overwritten with fresh values. Which approach correctly updates the feature table using the FeatureEngineeringClient?

Accepted Answer

Call `fe.write_table(name='main.retail.customer_features', df=updated_df, mode='merge')` so only the provided rows are upserted by primary key while other rows are preserved.

Answer

Call `fe.write_table(name='main.retail.customer_features', df=updated_df, mode='overwrite')` to replace the whole table each night.

Answer

Call `fe.create_table(...)` again each night with the updated DataFrame to append the new rows.

Answer

Use `spark.write.mode('append').saveAsTable('main.retail.customer_features')` to add the updated rows directly.