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This page includes example notebooks that illustrate the feature engineering workflow in Databricks for both Feature Engineering in Unity Catalog and Workspace Feature Store scenarios.
Note
With Databricks Runtime 13.3 LTS and above, any Delta table in Unity Catalog that has a primary key is automatically a feature table that you can use for model training and inference. When you use a table registered in Unity Catalog as a feature table, all Unity Catalog capabilities are automatically available to the feature table.
Basic feature engineering example
The basic feature engineering example notebook steps you through how to create a feature table, use it to train a model, and then perform batch scoring using automatic feature lookup. It also introduces you to the Feature Engineering UI and shows how you can use it to search for features and understand how features are created and used.
Use the following notebook if your workspace is not enabled for Unity Catalog.
Taxi example with point-in-time lookup
The taxi example notebook illustrates the process of creating features, updating them, and using them for model training and batch inference.
Use the following notebook if your workspace is not enabled for Unity Catalog.
Create Feature Serving endpoints
The following notebook illustrates how to use the Databricks SDK to create a Feature Serving endpoint using Databricks Online Tables.
The following notebook illustrates how to use the Databricks SDK to create a Feature Serving endpoint using a third-party online store.
