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The dbt Semantic Layer is built directly into dbt Cloud to eliminate metric inconsistencies, reduce manual SQL work, and deliver trusted, consistent metrics to end users—all while maintaining complete governance and lineage through the familiar dbt Cloud workflows your team already uses. It ensures governed models, query optimization, and seamless integration with your BI tools, AI systems, and embedded applications. The dbt Semantic Layer leverages Metricflow to auto-generate SQL on the fly, enabling you to perform complex calculations dynamically without building redundant aggregation tables, and allowing you to slice metrics across any dimension with ease. When business requirements change, you can easily update metric definitions without extensive downstream rebuilds, saving weeks of engineering time. Follow along as we demonstrate setting up the semantic layer, creating semantic models, leveraging caching, and integrating with tools like Google Sheets and Tableau. Unlock the full potential of your data with centralized, governed metrics that offer secure, reliable insights. Learn more about the dbt Semantic Layer https://www.getdbt.com/product/semant... Timestamps: 00:00 Introduction to dbt Semantic Layer 00:23 Setting Up the Semantic Layer 00:51 Creating Your First Semantic Model 01:06 Defining Metrics and Specifications 01:50 Optimizing Query Performance 02:17 Ensuring Data Consistency and Governance 02:52 Advanced Features and Integrations 03:34 Conclusion and Further Resources