Looker Data Modeling Mastery Hub: The Industry Foundation Pr

When designing a new Explores in Looker for a complex analytical requirement, which of the following modeling principles is MOST crucial to ensure performance and maintainability, especially when dealing with large datasets?

, while sometimes useful for very specific slices, can lead to duplicated logic and maintenance nightmares if the underlying data needs change. Distractor D, relying solely on direct joins, is often inefficient for complex analytical scenarios as it forces Looker to perform all joins and aggregations on the fly, potentially scanning large amounts of raw dat

Consider a scenario where a Looker Explores is experiencing slow query performance. If the underlying LookML model utilizes a "view" that is derived from a complex SQL query with multiple subqueries and aggregations, what is the MOST effective strategy to investigate and potentially optimize this performance bottleneck?