dbt-transformation-patterns

Production-ready patterns for dbt model organization, testing, documentation, and incremental processing. Production-ready patterns for dbt (data build tool) including model organization, testing strategies, 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 - 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 - Use staging layer - Clean data once, use everywhere - Test aggressively - Not null, unique, relationships - Document everything - Column descriptions, model descriptions - Use incremental - For tables > 1M rows - Version control - dbt project in Git -...