In this example, we're going to show you how Apache Hamilton can help you structure your Feast repository and bring tighter coupling between your feature transformations (code) and feature store (data).

• Feast is a feature store, which is an ML-specific stack component, that helps store and serve features (offline vs. online, batch vs. stream). It keeps a registry of features scattered across storage sources (database, data warehouse, streaming, etc.) and facilitates data retrieval and joining. Features need to be computed separately, typically in an SQL pipeline or a Python dataframe library and then be pushed to the Feast feature store(Feast FAQ).
• Apache Hamilton is a data transformation micro-framework. It helps one write Python code that is modular and reusable, and that expresses a DAG of execution. It was initially developed for large dataframes with hundreds of columns for machine learning while preserving strong lineage capabilities (high-level comparison).

• /default_feature_store is the quickstart example you can generate by calling feast init default. It is presented here as a reference point to compare with Apache Hamilton + Feast alternatives.
• /simple_feature_store is a 1-to-1 reimplementation of /default_feature_store. You will notice that adding Apache Hamilton helps make explicit the dependencies between Feast objects therefore increasing readability and maintainability.
• /integration_feature_store extends the /simple_feature_store example by adding the feature transformation code using Apache Hamilton and directly integrating with Feast.
• retrieval.ipynb in /integration_feature_store/feature_repo shows how to retrieve features from Feast and highlights the benefits of end-to-end visibility with Apache Hamilton.

• Hands-on workshop: https://github.com/feast-dev/feast-workshop