Overview
dbt (data build tool) models analytics transformations as software: modular SQL, packages, tests, and documentation generated from project metadata. dbt Core runs locally and in CI; dbt Cloud adds orchestration, job observability, and the Metric Flow powered semantic layer for consistent business metrics (dbt documentation).
Adopt for any team building curated tables in a cloud warehouse or lakehouse who needs reproducible pipelines, pull request review for data logic, and contracts between analytics engineering and downstream AI or BI consumers.
Adoption Signals
- Most greenfield lakehouse programs standardize bronze or silver layers in dbt projects.
- Semantic layer pilots expose the same metrics to BI tools and Python agents via APIs.
- Elementary and dbt Cloud observability integrations catch schema drift and test failures early.
- Fusion and performance initiatives reduce parse and compile times for mega-repos.
Risks
- Untested incremental models can silently duplicate or drop rows in production.
- Overly permissive warehouse roles in CI service accounts widen blast radius.
- Metric definitions without owners recreate conflicting KPIs across departments.
- Package version drift between projects complicates centralized upgrades.
Pros & Cons
- Version-controlled SQL transformations with tests, documentation, and lineage are the de facto standard for warehouse analytics.
- dbt Cloud adds scheduling, observability, and semantic layer APIs for governed metrics consumption.
- Adapter ecosystem spans Snowflake, Databricks, BigQuery, Redshift, and open lakehouse engines.
- Complex Jinja macros can become undebuggable without style guides and code review discipline.
- Semantic layer adoption still requires organizational agreement on metric definitions and owners.
- Running large projects without CI performance tuning leads to slow merge feedback.
Recommendation
Adopt dbt as the transformation layer for warehouse and lakehouse analytics, with mandatory tests on incremental models and documented owners per semantic metric. Use dbt Cloud when you need centralized scheduling and observability; stay on Core plus your orchestrator when cost or data residency requires it.
Sources