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Scaling dbt usually starts with a simple goal: make transformations more reliable. But as your data stack grows, dbt quickly becomes the backbone of the entire pipeline — and suddenly you’re dealing with long-running models, broken dependencies, and unpredictable builds. The good news is that dbt can scale gracefully. In this guide, we’ll explore the proven strategies modern data teams use to scale dbt using advanced materializations, the semantic layer, CI/CD, and orchestrators.