A Logical Feature Store is a conceptual framework enabling efficient management, storage, and retrieval of features used in machine learning models. It provides an abstraction layer that simplifies the feature engineering process across multiple workflows and experiments. It facilitates data consistency and reusability by managing a metadata catalog that dictates how features are transformed, computed, and aggregated in different contexts, enhancing operational efficiency and reducing redundancy.