dbt

dbt (Data Build Tool)

SQL-based transformations for analytics engineering.

Overview

dbt (data build tool) enables analytics engineers to transform data in their warehouses using SQL. It's the standard tool for analytics engineering.

Key Concepts

Models

Model = SQL transformation

Example:

-- models/staging/stg_orders.sql
{{ config(materialized='view') }}

select
    order_id,
    user_id,
    amount,
    created_at
from {{ source('raw', 'orders') }}
where status = 'completed'

Tests

Test = Data quality check

Example:

# models/schema.yml
models:
  - name: stg_orders
    columns:
      - name: order_id
        tests:
          - unique
          - not_null

Macros

Macro = Reusable SQL

Example:

-- macros/date_spine.sql
{% macro date_spine(start_date, end_date) %}
    select date_day
    from {{ ref('date_spine') }}
    where date_day between '{{ start_date }}' and '{{ end_date }}'
{% endmacro %}

Best Practices

1. Staging models - Clean raw data first
2. Intermediate models - Build incrementally
3. Marts - Final business logic
4. Tests - Test everything
5. Documentation - Document all models

Related Topics

• Airflow - Orchestrating dbt
• Data Orchestration - Orchestration overview

---

Next: Data Processing →