dbt-transformation-patterns

name	dbt-transformation-patterns
description	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.
version	1.0.0
cluster	databases-data

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

Patterns

Pattern 1: Source Definitions

# models/staging/stripe/_stripe__sources.yml
version: 2

sources:
  - name: stripe
    description: Raw Stripe data loaded via Fivetran
    database: raw
    schema: stripe
    loader: fivetran
    loaded_at_field: _fivetran_synced
    freshness:
      warn_after: {count: 12, period: hour}
      error_after: {count: 24, period: hour}
    tables:
      - name: customers
        description: Stripe customer records
        columns:
          - name: id
            description: Primary key
            tests:
              - unique
              - not_null
          - name: email
              
            
               

        
           
        
            
            
               
               
            
            
               
               
                    
                   

Pattern 2: Staging Models

-- models/staging/stripe/stg_stripe__customers.sql
with source as (
    select * from {{ source('stripe', 'customers') }}
),

renamed as (
    select
        -- ids
        id as customer_id,

        -- strings
        lower(email) as email,
        name as customer_name,

        -- timestamps
        created as created_at,

        -- metadata
        _fivetran_synced as _loaded_at

    from source
)

select * from renamed

-- models/staging/stripe/stg_stripe__payments.sql
{{
    config(
        materialized='incremental',
        unique_key='payment_id',
        on_schema_change='append_new_columns'
    )
}}

with source as (
    select * from {{ source('stripe', 'payments') }}

    {% if is_incremental() %}
    where _fivetran_synced > (select max(_loaded_at) from {{ this }})
    {% endif %}
),

renamed as (
    select
        -- ids
        id as payment_id,
        customer_id,
        invoice_id,

        -- amounts (convert cents to dollars)
        amount / 100.0 as amount,
        amount_refunded / 100.0 as amount_refunded,

        -- status
        status as payment_status,

        -- timestamps
        created as created_at,

        -- metadata
        _fivetran_synced as _loaded_at

    from source
)

select * from renamed

Pattern 3: Intermediate Models

-- models/intermediate/finance/int_payments_pivoted_to_customer.sql
with payments as (
    select * from {{ ref('stg_stripe__payments') }}
),

customers as (
    select * from {{ ref('stg_stripe__customers') }}
),

payment_summary as (
    select
        customer_id,
        count(*) as total_payments,
        count(case when payment_status = 'succeeded' then 1 end) as successful_payments,
        sum(case when payment_status = 'succeeded' then amount else 0 end) as total_amount_paid,
        min(created_at) as first_payment_at,
        max(created_at) as last_payment_at
    from payments
    group by customer_id
)

select
    customers.customer_id,
    customers.email,
    customers.created_at as customer_created_at,
    coalesce(payment_summary.total_payments, 0) as total_payments,
    (payment_summary.successful_payments, )  successful_payments,
    (payment_summary.total_amount_paid, )  lifetime_value,
    payment_summary.first_payment_at,
    payment_summary.last_payment_at

 customers
  payment_summary  (customer_id)

Pattern 4: Mart Models (Dimensions and Facts)

-- models/marts/core/dim_customers.sql
{{
    config(
        materialized='table',
        unique_key='customer_id'
    )
}}

with customers as (
    select * from {{ ref('int_payments_pivoted_to_customer') }}
),

orders as (
    select * from {{ ref('stg_shopify__orders') }}
),

order_summary as (
    select
        customer_id,
        count(*) as total_orders,
        sum(total_price) as total_order_value,
        min(created_at) as first_order_at,
        max(created_at) as last_order_at
    from orders
    group by customer_id
),

final as (
    select
        -- surrogate key
        {{ dbt_utils.generate_surrogate_key(['customers.customer_id']) }} as customer_key,

        -- natural key
        customers.customer_id,

        -- attributes
        customers.email,
        customers.customer_created_at,

        -- payment metrics
        customers.total_payments,
        customers.successful_payments,
        customers.lifetime_value,
        customers.first_payment_at,
        customers.last_payment_at,

        -- order metrics
        coalesce(order_summary.total_orders, )  total_orders,
        (order_summary.total_order_value, )  total_order_value,
        order_summary.first_order_at,
        order_summary.last_order_at,

        
        
             customers.lifetime_value    
             customers.lifetime_value    
             
          customer_tier,

        
          _loaded_at

     customers
      order_summary  (customer_id)
)

   

-- models/marts/core/fct_orders.sql
{{
    config(
        materialized='incremental',
        unique_key='order_id',
        incremental_strategy='merge'
    )
}}

with orders as (
    select * from {{ ref('stg_shopify__orders') }}

    {% if is_incremental() %}
    where updated_at > (select max(updated_at) from {{ this }})
    {% endif %}
),

customers as (
    select * from {{ ref('dim_customers') }}
),

final as (
    select
        -- keys
        orders.order_id,
        customers.customer_key,
        orders.customer_id,

        -- dimensions
        orders.order_status,
        orders.fulfillment_status,
        orders.payment_status,

        -- measures
        orders.subtotal,
        orders.tax,
        orders.shipping,
        orders.total_price,
        orders.total_discount,
        orders.item_count,

        -- timestamps
        orders.created_at,
        orders.updated_at,
        orders.fulfilled_at,

        -- metadata
        current_timestamp as _loaded_at

    from orders
    left join customers on orders.customer_id = customers.customer_id
)

   

Pattern 5: Testing and Documentation

# models/marts/core/_core__models.yml
version: 2

models:
  - name: dim_customers
    description: Customer dimension with payment and order metrics
    columns:
      - name: customer_key
        description: Surrogate key for the customer dimension
        tests:
          - unique
          - not_null

      - name: customer_id
        description: Natural key from source system
        tests:
          - unique
          - not_null

      - name: email
        description: Customer email address
        tests:
          - not_null

      -  
               
        
           
               [, , ]

        
             
        
           
               

    
           
    
       
           
           
           
    
        
        
           
           
        
        
           
           
               
               

Pattern 6: Macros and DRY Code

-- macros/cents_to_dollars.sql
{% macro cents_to_dollars(column_name, precision=2) %}
    round({{ column_name }} / 100.0, {{ precision }})
{% endmacro %}

-- macros/generate_schema_name.sql
{% macro generate_schema_name(custom_schema_name, node) %}
    {%- set default_schema = target.schema -%}
    {%- if custom_schema_name is none -%}
        {{ default_schema }}
    {%- else -%}
        {{ default_schema }}_{{ custom_schema_name }}
    {%- endif -%}
{% endmacro %}

-- macros/limit_data_in_dev.sql
{% macro limit_data_in_dev(column_name, days=3) %}
    {% if target.name == 'dev' %}
        where {{ column_name }} >= dateadd(day, -{{ days }}, current_date)
    { endif }
{ endmacro }


   {{ () }}
{{ limit_data_in_dev() }}

Pattern 7: Incremental Strategies

-- Delete+Insert (default for most warehouses)
{{
    config(
        materialized='incremental',
        unique_key='id',
        incremental_strategy='delete+insert'
    )
}}

-- Merge (best for late-arriving data)
{{
    config(
        materialized='incremental',
        unique_key='id',
        incremental_strategy='merge',
        merge_update_columns=['status', 'amount', 'updated_at']
    )
}}

-- Insert Overwrite (partition-based)
{{
    config(
        materialized='incremental',
        incremental_strategy='insert_overwrite',
        partition_by={
            "field": "created_date",
            "data_type": "date",
            "granularity": "day"
        }
    )
}}

select
    *,
    date(created_at) as created_date
from {{ ref('stg_events') }}

{% if is_incremental() %}
where created_date >= dateadd(day, -3, current_date)
{% endif %}

dbt Commands

# Development
dbt run                          # Run all models
dbt run --select staging         # Run staging models only
dbt run --select +fct_orders     # Run fct_orders and its upstream
dbt run --select fct_orders+     # Run fct_orders and its downstream
dbt run --full-refresh           # Rebuild incremental models

# Testing
dbt test                         # Run all tests
dbt test --select stg_stripe     # Test specific models
dbt build                        # Run + test in DAG order

# Documentation
dbt docs generate                # Generate docs
dbt docs serve                   # Serve docs locally

# Debugging
dbt compile                      # Compile SQL without running
dbt debug                        # Test connection
dbt ls --select tag:critical     # List models by tag

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

Resources

• dbt Documentation
• dbt Best Practices
• dbt-utils Package
• dbt Discourse