dbt Transformation Patterns Skill

dbt Transformation Patterns

Production-ready patterns for dbt (data build tool) including model organization, testing strategies, documentation, and incremental processing.

When to Use This Skill

Building data transformation pipelines with dbt
Organizing models into staging, intermediate, and marts layers
Implementing data quality tests
Creating incremental models for large datasets
Documenting data models and lineage
Setting up dbt project structure

Core Concepts

1. Model Layers (Medallion Architecture)

sources/          Raw data definitions
    ↓
staging/          1:1 with source, light cleaning
    ↓
intermediate/     Business logic, joins, aggregations
    ↓
marts/            Final analytics tables

2. Naming Conventions

| Layer | Prefix | Example | | ------------ | -------------- | ----------------------------- | | Staging | stg_ | stg_stripe__payments | | Intermediate | int_ | int_payments_pivoted | | Marts | dim_, fct_ | dim_customers, fct_orders |

Quick Start

# dbt_project.yml
name: "analytics"
version: "1.0.0"
profile: "analytics"

model-paths: ["models"]
analysis-paths: ["analyses"]
test-paths: ["tests"]
seed-paths: ["seeds"]
macro-paths: ["macros"]

vars:
  start_date: "2020-01-01"

models:
  analytics:
    staging:
      +materialized: view
      +schema: staging
    intermediate:
      +materialized: ephemeral
    marts:
      +materialized: table
      +schema: analytics

# Project structure
models/
├── staging/
│   ├── stripe/
│   │   ├── _stripe__sources.yml
│   │   ├── _stripe__models.yml
│   │   ├── stg_stripe__customers.sql
│   │   └── stg_stripe__payments.sql
│   └── shopify/
│       ├── _shopify__sources.yml
│       └── stg_shopify__orders.sql
├── intermediate/
│   └── finance/
│       └── int_payments_pivoted.sql
└── marts/
    ├── core/
    │   ├── _core__models.yml
    │   ├── dim_customers.sql
    │   └── fct_orders.sql
    └── finance/
        └── fct_revenue.sql

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.

Best Practices

Do's

Use staging layer - Clean data once, use everywhere
Test aggressively - Not null, unique, relationships
Document everything - Column descriptions, model descriptions
Use incremental - For tables > 1M rows
Version control - dbt project in Git

Don'ts

Don't skip staging - Raw → mart is tech debt
Don't hardcode dates - Use {{ var('start_date') }}
Don't repeat logic - Extract to macros
Don't test in prod - Use dev target
Don't ignore freshness - Monitor source data

Agent Skills: dbt Transformation Patterns

Install this agent skill to your local

Skill Files