What is the medallion architecture in dbt?

It is a data design pattern that organizes models into three distinct layers: Staging (raw data cleaning), Intermediate (business logic and joins), and Marts (final, analytics-ready tables).

Does it support DRY code principles?

Absolutely. It includes patterns for creating and utilizing dbt macros to automate repetitive SQL tasks like unit conversions, schema management, and environment-based logic.

How does this skill help with large datasets?

It includes specific patterns for incremental materialization, allowing you to update your tables by only processing new data rather than performing full refreshes.

Can I use this for data quality testing?

Yes, the skill provides standardized YAML configurations for implementing unique, not_null, relationship, and custom expression tests to ensure data integrity.

dbt Transformation Patterns

Name: dbt Transformation Patterns
Author: pur3v4d3r

bypur3v4d3r

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データベース管理

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

概要

This skill empowers developers to build professional-grade data transformation pipelines using dbt (data build tool). It provides standardized implementation patterns for the Medallion Architecture—including staging, intermediate, and marts layers—alongside robust testing frameworks and documentation strategies. By incorporating best practices for incremental processing and DRY (Don't Repeat Yourself) code via macros, it ensures data warehouses remain performant, maintainable, and reliable as they scale from raw sources to final business intelligence assets.

主な機能

1 GitHub stars
Medallion architecture organization (Staging, Intermediate, Marts)
Automated testing and documentation YAML configurations
Reusable macros for modular and DRY data engineering code
Production-ready SQL patterns for Dimension and Fact tables
Advanced incremental loading strategies including Merge and Partitioning

ユースケース

Refactoring monolithic SQL scripts into a standardized, layered architecture
Building a new data warehouse from raw source data using dbt best practices
Implementing automated data quality checks and lineage documentation

概要

主な機能

1 GitHub stars
Medallion architecture organization (Staging, Intermediate, Marts)
Automated testing and documentation YAML configurations
Reusable macros for modular and DRY data engineering code
Production-ready SQL patterns for Dimension and Fact tables
Advanced incremental loading strategies including Merge and Partitioning

ユースケース

Refactoring monolithic SQL scripts into a standardized, layered architecture
Building a new data warehouse from raw source data using dbt best practices
Implementing automated data quality checks and lineage documentation