Agent skill
dbt
dbt (data build tool) transforms data in your warehouse using SQL SELECT statements. Learn project setup, models, tests, documentation, incremental materializations, and integration with data warehouses like PostgreSQL, BigQuery, and Snowflake.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/dbt-terminalskills-skills
Metadata
Additional technical details for this skill
- tags
-
dbt data-transformation sql data-warehouse analytics
- author
- terminal-skills
- version
- 1.0.0
- category
- data-ai
SKILL.md
dbt
dbt lets analytics engineers transform data by writing SQL SELECT statements. It handles materialization (tables, views, incremental), testing, documentation, and lineage tracking.
Installation
# Install dbt with PostgreSQL adapter
pip install dbt-postgres
# Or with other adapters
pip install dbt-bigquery
pip install dbt-snowflake
# Initialize a new project
dbt init my_project
cd my_project
Project Structure
my_project/
├── dbt_project.yml # Project configuration
├── profiles.yml # Connection profiles (usually in ~/.dbt/)
├── models/
│ ├── staging/ # Raw data cleaning
│ │ ├── _staging.yml # Schema + tests for staging models
│ │ ├── stg_users.sql
│ │ └── stg_orders.sql
│ └── marts/ # Business logic
│ ├── _marts.yml
│ └── fct_revenue.sql
├── tests/ # Custom data tests
├── macros/ # Reusable SQL macros
└── seeds/ # CSV files to load
Configuration
# dbt_project.yml: Project configuration
name: my_project
version: '1.0.0'
profile: my_project
models:
my_project:
staging:
+materialized: view
+schema: staging
marts:
+materialized: table
+schema: analytics
# profiles.yml: Database connection (~/.dbt/profiles.yml)
my_project:
target: dev
outputs:
dev:
type: postgres
host: localhost
port: 5432
user: analyst
password: "{{ env_var('DBT_PASSWORD') }}"
dbname: analytics
schema: dev
threads: 4
prod:
type: postgres
host: prod-db.example.com
port: 5432
user: dbt_prod
password: "{{ env_var('DBT_PROD_PASSWORD') }}"
dbname: analytics
schema: public
threads: 8
Staging Models
-- models/staging/stg_users.sql: Clean raw user data
WITH source AS (
SELECT * FROM {{ source('raw', 'users') }}
),
cleaned AS (
SELECT
id AS user_id,
LOWER(TRIM(email)) AS email,
name,
created_at::timestamp AS signed_up_at,
CASE WHEN status = 'active' THEN TRUE ELSE FALSE END AS is_active
FROM source
WHERE email IS NOT NULL
)
SELECT * FROM cleaned
-- models/staging/stg_orders.sql: Clean raw order data
SELECT
id AS order_id,
user_id,
amount_cents / 100.0 AS amount,
status,
created_at::timestamp AS ordered_at
FROM {{ source('raw', 'orders') }}
WHERE status != 'test'
Mart Models
-- models/marts/fct_revenue.sql: Revenue fact table
{{
config(
materialized='incremental',
unique_key='order_date',
on_schema_change='sync_all_columns'
)
}}
WITH orders AS (
SELECT * FROM {{ ref('stg_orders') }}
{% if is_incremental() %}
WHERE ordered_at > (SELECT MAX(order_date) FROM {{ this }})
{% endif %}
),
daily AS (
SELECT
DATE_TRUNC('day', ordered_at)::date AS order_date,
COUNT(*) AS total_orders,
COUNT(DISTINCT user_id) AS unique_customers,
SUM(amount) AS total_revenue,
AVG(amount) AS avg_order_value
FROM orders
WHERE status = 'completed'
GROUP BY 1
)
SELECT * FROM daily
Schema and Tests
# models/staging/_staging.yml: Define sources, columns, and tests
version: 2
sources:
- name: raw
schema: public
tables:
- name: users
loaded_at_field: created_at
freshness:
warn_after: {count: 12, period: hour}
error_after: {count: 24, period: hour}
- name: orders
models:
- name: stg_users
description: Cleaned user data
columns:
- name: user_id
tests: [unique, not_null]
- name: email
tests: [unique, not_null]
- name: stg_orders
columns:
- name: order_id
tests: [unique, not_null]
- name: status
tests:
- accepted_values:
values: ['pending', 'completed', 'cancelled', 'refunded']
CLI Commands
# commands.sh: Common dbt CLI commands
# Run all models
dbt run
# Run specific model and its upstream dependencies
dbt run --select +fct_revenue
# Run tests
dbt test
# Generate and serve documentation
dbt docs generate
dbt docs serve --port 8081
# Check source freshness
dbt source freshness
# Full build (run + test + snapshot)
dbt build
# Run against production
dbt run --target prod
Macros
-- macros/cents_to_dollars.sql: Reusable macro for currency conversion
{% macro cents_to_dollars(column_name) %}
({{ column_name }} / 100.0)::numeric(10,2)
{% endmacro %}
-- Usage in a model: SELECT {{ cents_to_dollars('amount_cents') }} AS amount
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