How does it handle data quality and testing?

The skill provides patterns for schema tests, unique/null constraints, relationship testing between models, and custom data validation macros.

Can this skill help with dbt documentation?

Yes, it includes standardized YAML patterns for documenting models, sources, and columns to ensure clear data lineage and discovery.

Does it support incremental loading for large datasets?

Yes, it includes patterns for various incremental strategies including merge, delete+insert, and partition-based insert-overwrite.

What architecture does this dbt skill follow?

It implements the Medallion architecture, organizing data into staging (raw cleaning), intermediate (business logic), and marts (analytics-ready) layers.

dbt Transformation Patterns

Name: dbt Transformation Patterns
Author: wshobson

bywshobson

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23,194

Gestión de Bases de Datos

Implements production-ready dbt patterns for scalable data modeling, automated testing, and analytics engineering workflows.

Acerca de

This skill equips Claude with specialized knowledge of dbt (data build tool) best practices, enabling the architecting of robust, scalable analytics pipelines. It provides standardized implementation patterns for Medallion architecture (staging, intermediate, and marts layers), advanced incremental loading strategies, and comprehensive data quality testing. Whether you are refactoring legacy SQL or building a new data warehouse, this skill ensures your models follow strict naming conventions, maintainable project structures, and DRY code principles through macro development.

Características Principales

Medallion architecture implementation (staging, intermediate, marts)
Automated data quality testing and documentation patterns
Reusable macro development for DRY SQL transformations
Standardized naming conventions and directory structures
Advanced incremental model strategies for large-scale data
23,194 GitHub stars

Casos de Uso

Migrating legacy monolithic SQL scripts into modular dbt models
Implementing automated data freshness and relationship testing
Building scalable data transformation pipelines from raw cloud sources

Acerca de

Características Principales

Medallion architecture implementation (staging, intermediate, marts)
Automated data quality testing and documentation patterns
Reusable macro development for DRY SQL transformations
Standardized naming conventions and directory structures
Advanced incremental model strategies for large-scale data
23,194 GitHub stars

Casos de Uso

Migrating legacy monolithic SQL scripts into modular dbt models
Implementing automated data freshness and relationship testing
Building scalable data transformation pipelines from raw cloud sources