What testing practices does it recommend?

It emphasizes testing the grain of models, enforcing foreign key integrity, and encoding business rules as dbt tests with specific severity levels for monitoring vs. blockers.

Does it support Type-2 Slowly Changing Dimensions?

Yes, it includes patterns for implementing Type-2 dimensions with validity intervals (valid_from/valid_to) and surrogate key generation.

How does it handle documentation?

It enforces a 'one model, one schema file' rule and provides templates for describing the grain, column definitions, and using dbt doc macros for consistent definitions.

How does this skill help with dbt projects?

It provides structural guidance on model layering, naming conventions, and materialization strategies to ensure your dbt project remains scalable and organized.

Data Warehousing with dbt

Name: Data Warehousing with dbt
Author: btimothy-har

bybtimothy-har

0•

데이터베이스 관리

Builds and maintains scalable, well-documented data warehouses using dimensional modeling principles and dbt best practices.

This skill provides a comprehensive framework for designing and implementing modern data warehouses using dbt (data build tool). It enforces a layered architecture—separating base, intermediate, domain, and mart models—while applying Kimball-style dimensional modeling techniques like surrogate keys and Type-2 dimensions. By automating documentation patterns and testing strategies, ranging from grain uniqueness to business rule validation, this skill ensures that data teams can produce maintainable, high-quality data pipelines that serve as reliable public APIs for business intelligence and analytics.

주요 기능

01Materialization strategy guidance for incremental and snapshot models

02Comprehensive data quality testing for grain, relationships, and business logic

03Layered architecture enforcement (Base, Intermediate, Domain, Mart)

04Kimball-style dimensional modeling with surrogate key generation

050 GitHub stars

06Standardized dbt documentation and schema file management

사용 사례

01Refactoring flat SQL scripts into a structured, layered dbt project

02Designing a new data warehouse from scratch using dbt

03Implementing Type-2 slowly changing dimensions to track historical data changes

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add btimothy-har/basecamp data-warehousing

For use in Claude.ai and ChatGPT

주요 기능

01Materialization strategy guidance for incremental and snapshot models

02Comprehensive data quality testing for grain, relationships, and business logic

03Layered architecture enforcement (Base, Intermediate, Domain, Mart)

04Kimball-style dimensional modeling with surrogate key generation

050 GitHub stars

06Standardized dbt documentation and schema file management

사용 사례

01Refactoring flat SQL scripts into a structured, layered dbt project

02Designing a new data warehouse from scratch using dbt

03Implementing Type-2 slowly changing dimensions to track historical data changes

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add btimothy-har/basecamp data-warehousing

For use in Claude.ai and ChatGPT