### Option 1: Entity-First Approach **Overview:** - Focuses on building views around entities in the data model. - Views are denormalized tables that combine measures and dimensions from various cubes to fully describe an entity. - Suitable for creating comprehensive representations of entities like `orders` or `line_items`. **Advantages:** - Provides a holistic view of entities. - Facilitates easier understanding for users familiar with entity relationships. - Allows for creating multiple views to avoid overwhelming users with too many dimensions. **Example:** A view named `orders_view` that includes various measures and dimensions related to orders, and optionally a `orders_with_users_view` to include user-related data. ### Option 2. Metrics-First Approach **Overview:** - Centers on measures (metrics) in the data model. - Views contain one measure along with relevant dimensions for grouping or filtering, typically named after the measure. - Enhances compatibility with BI tools by focusing on specific metrics. **Advantages:** - Clarifies data for consumers by focusing on specific metrics. - Improves integration with metrics-based BI tools. - Simplifies the creation of views for different time dimensions. **Example:** A view named `average_order_value` that includes the measure for average order value and relevant dimensions like status, created_at, city, age, and gender.