dbt-transformation-patterns

Name: dbt-transformation-patterns
Availability: InStock
Author: jhabbig

Organize dbt transformations into staging, intermediate, and mart layers.

Updated Apr 5, 2026

Search Tags:#documentation #data modeling #dbt #analytics engineering #warehouse #incremental models #data testing

Ready to run

npx skills add https://github.com/Jhabbig/Habbig --skill dbt-transformation-patterns-jhabbig

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dbt-transformation-patterns
Source: https://github.com/Jhabbig/Habbig/tree/main/.claude/plugins/wshobson/data-engineering/skills/dbt-transformation-patterns
Command: npx skills add https://github.com/Jhabbig/Habbig --skill dbt-transformation-patterns-jhabbig

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps analytics engineers turn raw warehouse data into dependable dbt models with clear structure, testing, and documentation so transformation work stays maintainable and production-ready.

Core Features & Use Cases

Layered Model Design: Organize projects into staging, intermediate, and marts layers for cleaner ownership and logic flow.
Testing and Documentation: Define source freshness, column tests, model descriptions, and relationship checks to improve trust in downstream data.
Incremental Processing: Apply merge, delete-and-insert, or partition-based strategies for large or frequently changing datasets.
Use Case: Build a customer analytics mart from Stripe and Shopify-style sources, then validate keys, freshness, and revenue metrics before publishing.

Quick Start

Ask for a dbt transformation plan that organizes your sources, models, tests, and incremental strategy for your warehouse dataset.

Frequently Asked Questions about dbt-transformation-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I structure dbt models for staging, intermediate, and marts layers?▼

Structuring dbt models into staging, intermediate, and marts layers separates raw source cleaning from business logic and final presentation, ensuring cleaner ownership and reliable logic flow for analytics engineering. This layered design keeps transformation work maintainable and production-ready.

What is the best way to configure incremental dbt models for large warehouse datasets?▼

Configuring incremental dbt models requires applying warehouse-aware strategies like merge, delete-and-insert, or partition-based approaches to efficiently process large or frequently changing datasets without rebuilding the entire table.

How do I add YAML-based tests and documentation to my dbt project?▼

Adding YAML-based tests and documentation to a dbt project involves defining source freshness, column tests, model descriptions, and relationship checks in YAML files to improve trust in downstream data and validate keys and metrics.

Can I use this dbt transformation pattern for Stripe and Shopify source data?▼

Yes, you can apply this dbt transformation pattern to Stripe and Shopify-style sources to build customer analytics marts, validate revenue metrics, and verify data freshness before publishing the downstream models.

When should I use incremental build scenarios instead of full refreshes in dbt?▼

Use incremental build scenarios in dbt for large or frequently changing datasets to avoid full refreshes; apply warehouse-aware strategies to merge or partition updates efficiently and maintain production-ready models.