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"dbt Semantic Layer: Defining Metrics Once and Serving Them Everywhere"
Metric drift isn’t a nuisance—it’s a systems failure that quietly erodes trust in analytics. This book is written for experienced analytics engineers, data platform teams, and BI enablement leaders who are tired of reconciling “the same metric” across dashboards, ad hoc SQL, and application code. You’ll get a rigorous mental model for why semantic layers exist, where dbt’s Semantic Layer fits in the modern warehouse-centric stack, and how MetricFlow turns centralized definitions into consistent, auditable results across consumers.
You’ll learn to design semantic models that are resilient under real-world joins and changing grains: entities as join contracts, dimensions and time slicing, and measures that remain aggregation-safe. From there, the book moves into governed metric design: metric types (simple, ratio, cumulative, derived), composition patterns that stay DRY at scale, and change management practices that prevent breaking downstream stakeholders. It then shows how metrics are actually served—via GraphQL and JDBC/Arrow Flight SQL—covering metadata discovery, environment scoping, authentication, and client patterns that survive evolving definitions.
You should already be fluent in dbt modeling and warehouse fundamentals; the focus here is the deep end: debugging generated SQL, mitigating fan-out and semi-additivity hazards, and operating the semantic layer in production with performance levers, observability, and migration guidance from legacy dbt metrics.
"dbt Semantic Layer: Defining Metrics Once and Serving Them Everywhere"
Metric drift isn’t a nuisance—it’s a systems failure that quietly erodes trust in analytics. This book is written for experienced analytics engineers, data platform teams, and BI enablement leaders who are tired of reconciling “the same metric” across dashboards, ad hoc SQL, and application code. You’ll get a rigorous mental model for why semantic layers exist, where dbt’s Semantic Layer fits in the modern warehouse-centric stack, and how MetricFlow turns centralized definitions into consistent, auditable results across consumers.
You’ll learn to design semantic models that are resilient under real-world joins and changing grains: entities as join contracts, dimensions and time slicing, and measures that remain aggregation-safe. From there, the book moves into governed metric design: metric types (simple, ratio, cumulative, derived), composition patterns that stay DRY at scale, and change management practices that prevent breaking downstream stakeholders. It then shows how metrics are actually served—via GraphQL and JDBC/Arrow Flight SQL—covering metadata discovery, environment scoping, authentication, and client patterns that survive evolving definitions.
You should already be fluent in dbt modeling and warehouse fundamentals; the focus here is the deep end: debugging generated SQL, mitigating fan-out and semi-additivity hazards, and operating the semantic layer in production with performance levers, observability, and migration guidance from legacy dbt metrics.
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