dbt-transformation-patterns
Production-ready patterns for dbt model organization, testing, documentation, and incremental processing.
- Implements medallion architecture with staging, intermediate, and marts layers using consistent naming conventions (stg_, int_, dim_, fct_) and materialization strategies
- Covers source definitions with freshness checks, data quality tests (unique, not_null, relationships), and comprehensive YAML documentation for lineage tracking
- Provides incremental model patterns including delete+insert, merge, and partition-based strategies for efficient processing of large datasets
- Includes macro examples for DRY code, schema generation, and development-specific filtering, plus dbt command reference for common workflows
dbt Transformation Patterns
Production-ready patterns for dbt (data build tool) including model organization, testing strategies, documentation, and incremental processing.
When to Use This Skill
- Building data transformation pipelines with dbt
- Organizing models into staging, intermediate, and marts layers
- Implementing data quality tests
- Creating incremental models for large datasets
- Documenting data models and lineage
- Setting up dbt project structure
Core Concepts
1. Model Layers (Medallion Architecture)
sources/ Raw data definitions
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