Use when creating or modifying dimensional dbt models in warehouse-backed analytics projects. Covers a four-layer warehouse architecture (sources/staging/core/marts), naming conventions, no-alias SQL rule, surrogate-key and missing-record patterns, incremental strategies, deduplication, and common project macros. Use when building fact tables, dimension tables, staging models, writing SQL, or desi
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dbt Strategy
Patterns for building dbt models in warehouse-backed analytics projects using Kimball-style dimensional modeling.
Layer Architecture
sources/ Source views — raw data from app DB, event stream, billing, CRM, LMS
↓
staging/ Intermediate transformations (keep minimal — new models go directly to core/)
↓
core/ Fact and dimension tables (main transformation layer) → tables
↓
marts/ Business aggregations built on top of core → tab
[Description truncada. Veja o README completo no GitHub.]