Understand the dbt Semantic Layer's vocabulary — metrics, dimensions, entities, and saved queries that centralise business logic.
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In a PR review, a data analyst asks why you defined metrics in dbt instead of in the BI tool. The architectural reason is:
The dbt Semantic Layer centralises metric definitions (revenue, churn, etc.) in dbt, exposing them via an API so all BI tools consume the same logic — eliminating metric discrepancies.
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A standup question: what is a MetricFlow entity and why does it matter for the Semantic Layer?
MetricFlow entities declare the join keys (primary or foreign) on semantic models. MetricFlow uses them to automatically construct multi-model joins when a query spans related metrics or dimensions.
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During a design review, a colleague defines a dimension on a semantic model. What does a dimension represent in the dbt Semantic Layer?
Dimensions in the Semantic Layer are descriptive attributes (country, plan, channel) used to filter or group metrics. They map to columns on the semantic model's underlying dbt model.
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A data engineer proposes using saved queries in dbt. In a team discussion, you explain that saved queries:
Saved queries bundle a set of metrics, dimensions, and filters into a named, reusable definition. Tools querying the Semantic Layer can reference them directly, reducing repeated query construction.
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In a PR, the metric definition uses type: ratio. A reviewer asks what a ratio metric does. The correct answer is:
A ratio metric divides a numerator measure by a denominator measure (e.g. conversion rate = conversions / sessions). Both measures must be defined on the same semantic model.