Our review
Provides dbt (data build tool) patterns for model organization, incremental strategies, and data testing.
Strengths
- Clear and scalable project structure (staging, intermediate, marts)
- Implementation of incremental strategies (merge, delete+insert)
- Column and source tests with freshness and constraints
- Built-in documentation through YAML and SQL
Limitations
- Partition syntax specific to BigQuery, not adapted for other warehouses
- Does not cover advanced macros or hooks
- No CI/CD or orchestration configuration
When starting or refactoring a dbt project that needs modular organization and incremental models.
For simple ad-hoc queries or an already well-structured project without incremental needs.
Security analysis
SafeThe skill provides patterns and code for dbt (data build tool) development. It does not contain any destructive commands, data exfiltration, encoded payloads, or instructions that would compromise system security. The allowed Bash commands are limited to standard dbt CLI operations.
No concerns found
Examples
Create a new dbt project with staging, intermediate, and marts layers using the patterns from the dbt-coder skill.Write an incremental dbt model for my events table using merge strategy and a unique key.Generate a source YAML file for my raw database with freshness checks and column tests.name: dbt-coder description: dbt (data build tool) patterns for model organization, incremental strategies, and testing. allowed-tools: Read Write Edit Grep Glob Bash
dbt-Coder
Patterns for dbt (data build tool) transform layer development.
Project Structure
my_dbt_project/
├── dbt_project.yml
├── profiles.yml
├── models/
│ ├── staging/ # 1:1 with sources, light transforms
│ │ ├── stg_orders.sql
│ │ └── _staging.yml
│ ├── intermediate/ # Joins, business logic
│ │ └── int_orders_enriched.sql
│ └── marts/ # Final consumption layer
│ ├── finance/
│ │ └── fct_revenue.sql
│ └── marketing/
│ └── dim_customers.sql
├── seeds/ # Static lookup data
├── snapshots/ # SCD Type 2
├── macros/ # Reusable SQL
└── tests/ # Custom tests
dbt_project.yml
name: 'my_project'
version: '1.0.0'
config-version: 2
profile: 'my_project'
model-paths: ["models"]
seed-paths: ["seeds"]
test-paths: ["tests"]
macro-paths: ["macros"]
snapshot-paths: ["snapshots"]
models:
my_project:
staging:
+materialized: view
+schema: staging
intermediate:
+materialized: ephemeral
marts:
+materialized: table
+schema: marts
Staging Models
-- models/staging/stg_orders.sql
-- Naming: stg_<source>_<entity>
with source as (
select * from {{ source('raw', 'orders') }}
),
renamed as (
select
-- Rename to consistent naming
id as order_id,
customer_id,
order_date,
total_amount as order_total,
-- Type casting
cast(status as varchar(50)) as order_status,
-- Timestamps
created_at,
updated_at
from source
)
select * from renamed
Source Definition
# models/staging/_sources.yml
version: 2
sources:
- name: raw
database: raw_db
schema: public
freshness:
warn_after: {count: 12, period: hour}
error_after: {count: 24, period: hour}
tables:
- name: orders
identifier: orders_table
columns:
- name: id
tests:
- unique
- not_null
- name: customers
Intermediate Models
-- models/intermediate/int_orders_enriched.sql
-- Join staging models, apply business logic
with orders as (
select * from {{ ref('stg_orders') }}
),
customers as (
select * from {{ ref('stg_customers') }}
),
products as (
select * from {{ ref('stg_products') }}
)
select
o.order_id,
o.order_date,
o.order_total,
c.customer_id,
c.customer_name,
c.customer_segment,
-- Business logic
case
when o.order_total >= 1000 then 'high_value'
when o.order_total >= 100 then 'medium_value'
else 'low_value'
end as order_tier
from orders o
left join customers c on o.customer_id = c.customer_id
Mart Models
-- models/marts/finance/fct_revenue.sql
-- Final aggregated fact table
{{ config(
materialized='table',
partition_by={
"field": "order_date",
"data_type": "date",
"granularity": "month"
}
) }}
with orders as (
select * from {{ ref('int_orders_enriched') }}
)
select
date_trunc('day', order_date) as revenue_date,
customer_segment,
order_tier,
count(*) as order_count,
sum(order_total) as total_revenue,
avg(order_total) as avg_order_value
from orders
group by 1, 2, 3
Incremental Models
-- models/marts/fct_events.sql
{{ config(
materialized='incremental',
unique_key='event_id',
incremental_strategy='merge' -- or 'delete+insert', 'append'
) }}
select
event_id,
user_id,
event_type,
event_timestamp,
properties
from {{ source('raw', 'events') }}
{% if is_incremental() %}
-- Only new/updated rows since last run
where event_timestamp > (select max(event_timestamp) from {{ this }})
{% endif %}
Snapshots (SCD Type 2)
-- snapshots/snap_customers.sql
{% snapshot snap_customers %}
{{
config(
target_schema='snapshots',
unique_key='customer_id',
strategy='timestamp',
updated_at='updated_at',
)
}}
select * from {{ source('raw', 'customers') }}
{% endsnapshot %}
Tests
# models/marts/_schema.yml
version: 2
models:
- name: fct_revenue
description: Daily revenue aggregations
columns:
- name: revenue_date
tests:
- not_null
- name: total_revenue
tests:
- not_null
- dbt_utils.accepted_range:
min_value: 0
tests:
# Model-level tests
- dbt_utils.unique_combination_of_columns:
combination_of_columns:
- revenue_date
- customer_segment
- order_tier
Custom Tests
-- tests/assert_positive_revenue.sql
-- Returns rows that fail the test
select
revenue_date,
total_revenue
from {{ ref('fct_revenue') }}
where total_revenue < 0
Macros
-- macros/cents_to_dollars.sql
{% macro cents_to_dollars(column_name) %}
round({{ column_name }} / 100.0, 2)
{% endmacro %}
-- Usage in model:
-- select {{ cents_to_dollars('amount_cents') }} as amount_dollars
-- macros/generate_schema_name.sql
{% macro generate_schema_name(custom_schema_name, node) %}
{% if custom_schema_name %}
{{ custom_schema_name }}
{% else %}
{{ target.schema }}
{% endif %}
{% endmacro %}
dbt Commands
# Run all models
dbt run
# Run specific model and dependencies
dbt run --select fct_revenue+
# Run models with tag
dbt run --select tag:finance
# Test all
dbt test
# Generate docs
dbt docs generate
dbt docs serve
# Freshness check
dbt source freshness
# Full refresh of incremental
dbt run --full-refresh --select fct_events
# Build (run + test)
dbt build
Best Practices
# 1. Use ref() for model references
# BAD: select * from schema.stg_orders
# GOOD: select * from {{ ref('stg_orders') }}
# 2. Use source() for raw tables
# BAD: select * from raw_db.orders
# GOOD: select * from {{ source('raw', 'orders') }}
# 3. Document models
models:
- name: fct_revenue
description: |
Daily revenue by segment. Grain: one row per day/segment/tier.
Updated daily by the finance_dag.
meta:
owner: data-team
pii: false
Packages
# packages.yml
packages:
- package: dbt-labs/dbt_utils
version: 1.1.1
- package: dbt-labs/codegen
version: 0.12.1
- package: calogica/dbt_expectations
version: 0.10.1
# Install packages
dbt deps
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