How does this skill help with large datasets?

It provides patterns for incremental materialization, enabling you to transform only new or updated records rather than reprocessing the entire dataset, which saves time and compute costs.

Does it support custom macros and DRY code?

Absolutely. It includes templates for Jinja macros that help you avoid code repetition for common tasks like currency conversion, schema naming, and development environment data limiting.

What is the Medallion architecture in dbt?

It is a data design pattern that organizes models into three layers: Staging (1:1 with source), Intermediate (business logic and joins), and Marts (final analytics-ready tables like dimensions and facts).

Can I use this for data quality and testing?

Yes, the skill includes standardized patterns for schema tests (unique, not_null, relationships) and data documentation to ensure high reliability and clear lineage.

dbt Transformation Patterns

Name: dbt Transformation Patterns
Author: drgaciw

bydrgaciw

Database Management

Implements production-ready dbt patterns for scalable data modeling and analytics engineering.

About

This skill provides a comprehensive framework for building robust data transformation pipelines using dbt (data build tool). It guides developers through implementing the Medallion architecture—organizing data into staging, intermediate, and marts layers—while enforcing consistent naming conventions and modular logic. By applying these patterns, users can automate data quality testing, generate comprehensive lineage documentation, and optimize performance through advanced incremental processing strategies and reusable Jinja macros.

Key Features

Medallion architecture implementation (staging, intermediate, marts)
Standardized naming conventions for data lineage
DRY code patterns using custom Jinja macros
Automated data quality testing and YAML documentation
0 GitHub stars
Advanced incremental processing and merge strategies

Use Cases

Refactoring monolithic SQL scripts into modular dbt projects
Building scalable data warehouses from raw source data
Implementing automated data quality checks for production pipelines

About

Key Features

Medallion architecture implementation (staging, intermediate, marts)
Standardized naming conventions for data lineage
DRY code patterns using custom Jinja macros
Automated data quality testing and YAML documentation
0 GitHub stars
Advanced incremental processing and merge strategies

Use Cases

Refactoring monolithic SQL scripts into modular dbt projects
Building scalable data warehouses from raw source data
Implementing automated data quality checks for production pipelines