Dbt Transformation Patterns

by wshobson46891e7e60daNo licenseListed Oct 8, 2026Updated Oct 8, 2026

Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.

Instructions onlyData & Analytics

AI-generated overview

Provides dbt analytics-engineering patterns for model layering, testing, documentation, and incremental processing.

What it does: This skill supplies guidance and conventions for building dbt data transformation projects. It covers medallion-style model layers (staging, intermediate, marts), naming prefixes, a sample dbt_project.yml, and a recommended project directory structure. It also lists best practices for testing, documentation, incremental models, and source freshness, with further detail in a references file.
When to use it: Use it when setting up or organizing a dbt project, defining staging and mart models, adding data quality tests, or implementing incremental models for large datasets. It is also relevant when documenting data models and lineage or establishing dbt project structure.
Requirements: No scripts are included; the skill is instructions only. Working with dbt requires a dbt installation and a configured data warehouse connection, though the skill itself only provides guidance.

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

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

2. Naming Conventions

Quick Start

yaml

# dbt_project.ymlname: "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

# dbt_project.ymlname: "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 structuremodels/├── 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

# Project structuremodels/├── 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

Source and attribution

Source:wshobson/agentsinplugins/data-engineering/skills/dbt-transformation-patternsat commit46891e7

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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

Dbt Transformation Patterns

dbt Transformation Patterns

When to Use This Skill

Core Concepts

1. Model Layers (Medallion Architecture)

2. Naming Conventions

Quick Start

Detailed patterns and worked examples

Best Practices

Do's

Don'ts

Source and attribution

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