Dbt Transformation Patterns

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

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
            description: Customer email
          - name: created
            description: Account creation timestamp

      - name: payments
        description: Stripe payment transactions
        columns:
          - name: id
            tests:
              - unique
              - not_null
          - name: customer_id
            tests:
              - not_null
              - relationships:
                  to: source('stripe', 'customers')
                  field: id

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',
       
    )
}}

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,
    coalesce(payment_summary.successful_payments, 0) as successful_payments,
    coalesce(payment_summary.total_amount_paid, 0) as lifetime_value,
    payment_summary.first_payment_at,
    payment_summary.last_payment_at

from customers
left join payment_summary using (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, 0) as total_orders,
        coalesce(order_summary.total_order_value, 0) as total_order_value,
        order_summary.first_order_at,
        order_summary.last_order_at,

        -- calculated fields
        case
            when customers.lifetime_value >= 1000 then 'high'
            when customers.lifetime_value >= 100 then 'medium'
            else 'low'
        end as customer_tier,

        -- timestamps
        current_timestamp as _loaded_at

    from customers
    left join order_summary using (customer_id)
)

select * from final

-- 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
)

select * from final

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

      - name: customer_tier
        description: Customer value tier based on lifetime value
        tests:
          - accepted_values:
              values: ["high", "medium", "low"]

      - name: lifetime_value
        description: Total amount paid by customer
        tests:
          - dbt_utils.expression_is_true:
              expression: ">= 0"

  - name: fct_orders
    description: Order fact table with all order transactions
    tests:
      - dbt_utils.recency:
          datepart: day
          field: created_at
          interval: 1
    columns:
      - name: order_id
        tests:
          - unique
          - not_null
      - name: customer_key
        tests:
          - not_null
          - relationships:
              to: ref('dim_customers')
              field: customer_key

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 %}

-- Usage in model
select * from {{ ref('stg_orders') }}
{{ limit_data_in_dev('created_at') }}

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