Scaling dbt in Production: Advanced Materializations, the Semantic Layer, CI/CD, and Orchestration
dbt often enters an organization as a breath of fresh air. SQL becomes modular, lineage becomes visible, and the analytics team starts shipping faster than ever. But as more models, developers, and stakeholders enter the picture, the cracks begin to show. A single PR can break a dozen downstream models. A delayed job can hold up dashboards used by leadership. Incremental models that used to run in minutes suddenly balloon into hour-long builds. Scaling dbt isn’t just about performance. It's about reliability, maintainability, and protecting the people who depend on your data. Once dbt becomes mission-critical, it has to behave like production-grade software. This guide walks through what modern data teams actually do to scale dbt — the real-world patterns that work, and the pitfalls to avoid.
