What feature does Looker use to calculate aggregations correctly even when joins result in a fanout?

The feature that Looker employs to calculate aggregations correctly in the presence of joins that could lead to a fanout is known as symmetric aggregates. This approach allows Looker to handle complex joins more effectively by ensuring that aggregations remain accurate, even when the dimensionality of the dataset is increased due to multiple related tables.

Symmetric aggregates work by treating aggregations consistently across different join paths. For example, if you have a dimension that can be reached through different joins, symmetric aggregates help maintain the integrity of the aggregated data, preventing inaccurate counts or sums due to duplicated rows that may occur with many-to-many relationships. This is especially crucial when designing reports or dashboards where precise calculations are necessary for meaningful insights.

While aggregate measures, fanout controls, and data normalization each play important roles in data modeling and management, they do not specifically address the challenge posed by joins that create fanouts as effectively as symmetric aggregates do. Aggregate measures focus on defining how to summarize data, fanout controls limit the data returned to avoid excessive duplicates, and data normalization involves structuring data to reduce redundancy. However, symmetric aggregates directly address the challenge of maintaining accurate aggregations across complex relationships.