Welcome to Mastering dbt

Transform Your Data Warehouse with Software Engineering Best Practices

Welcome to the comprehensive guide to dbt (data build tool) - the framework that's revolutionizing how data teams build and maintain analytics infrastructure. If you're tired of messy SQL scripts, unclear dependencies, and untested data transformations, you're in the right place.

What is dbt?

dbt enables data analysts and engineers to transform data in their warehouses more effectively. It allows you to write modular SQL, test your data, document your models, and version control your analytics code - bringing software engineering best practices to analytics.

-- A simple dbt model
-- models/marts/customers.sql

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

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

customer_orders as (
    select
        customers.customer_id,
        customers.first_name,
        customers.last_name,
        count(orders.order_id) as order_count,
        sum(orders.amount) as total_amount
    from customers
    left join orders
        on customers.customer_id = orders.customer_id
    group by 1, 2, 3
)

select * from customer_orders

Why dbt?

Transform Data in Your Warehouse

No need to move data around. dbt runs transformations directly in your data warehouse (Snowflake, BigQuery, Redshift, etc.).

SQL + Software Engineering

Write modular SQL with Jinja templating, version control with Git, and deploy with CI/CD pipelines.

Built-in Testing

Test your data assumptions automatically - ensure uniqueness, non-null values, relationships, and custom business logic.

Automatic Documentation

Generate and serve documentation for your entire data warehouse with lineage graphs showing data dependencies.

Incremental Processing

Process only new or changed data to reduce costs and improve performance.

Open Source & Enterprise Ready

dbt Core is open source. dbt Cloud provides enterprise features like scheduling, monitoring, and IDE.

The Modern Data Stack

dbt sits at the heart of the modern data stack:

graph LR
    A[Data Sources] -->|Extract & Load| B[Data Warehouse]
    B -->|Transform| C[dbt]
    C -->|Analytics Ready| D[BI Tools]
    C -->|Quality Checks| E[Data Tests]
    C -->|Documentation| F[Data Catalog]

    style C fill:#ff6b35,stroke:#333,stroke-width:3px

Extract & Load: Fivetran, Airbyte, Stitch Data Warehouse: Snowflake, BigQuery, Redshift, Databricks Transform: dbt ← You are here Visualize: Looker, Tableau, Metabase, Mode

What You'll Learn

This tutorial will take you from dbt beginner to expert through hands-on examples:

1. Getting Started - Install dbt and build your first project
2. Core Concepts - Understand models, sources, tests, and documentation
3. Data Modeling - Learn dimensional modeling, incremental models, and snapshots
4. Practical Examples - Build real analytics pipelines
5. Advanced Topics - Master Jinja, custom materializations, and performance optimization
6. Real-world Project - Build a complete data warehouse from scratch
7. Best Practices - Production-ready patterns and CI/CD

Who Is This Tutorial For?

• Data Analysts wanting to bring engineering rigor to their SQL
• Analytics Engineers building data transformation pipelines
• Data Engineers modernizing legacy ETL processes
• Data Scientists needing reliable, tested data for modeling
• Anyone interested in modern data engineering

Prerequisites

This tutorial assumes knowledge of:

• SQL - Intermediate level (JOINs, GROUP BY, CTEs)
• Basic command line - Running commands, navigating directories
• Git basics - Helpful but not required
• Data warehouse concepts - Tables, schemas, databases

The dbt Philosophy

dbt is built on several core principles:

1. SQL-First

Transformations are written in SQL, the language data practitioners already know.

2. Version Control Everything

Your analytics code lives in Git, just like application code.

3. Test Your Data

Data tests run automatically to catch issues before they reach production.

4. Document by Default

Documentation is generated from your code and metadata, not maintained separately.

5. Modularity

Break complex transformations into small, reusable, testable components.

"dbt is the T in ELT. It doesn't extract or load data, but it's extremely good at transforming data that's already loaded into your warehouse."

Key Features

Models

Transform data with SELECT statements. dbt handles the DDL/DML.

{{ config(materialized='table') }}

select
    customer_id,
    sum(order_total) as lifetime_value
from {{ ref('orders') }}
group by 1

Tests

Validate data quality automatically:

models:
  - name: customers
    columns:
      - name: customer_id
        tests:
          - unique
          - not_null
      - name: email
        tests:
          - unique

Documentation

Describe your data in YAML:

models:
  - name: customers
    description: One record per customer
    columns:
      - name: customer_id
        description: Primary key for customers

Lineage Graphs

Visualize dependencies automatically:

raw_customers → stg_customers → dim_customers → customer_analytics
                                        ↓
raw_orders    → stg_orders    ──────────┘

dbt in Action

Traditional Approach

-- create_customers_table.sql (run manually)
CREATE OR REPLACE TABLE analytics.customers AS
SELECT
    id,
    name,
    email,
    created_at
FROM raw.customers;

-- create_orders_summary.sql (run manually after customers)
CREATE OR REPLACE TABLE analytics.orders_summary AS
SELECT
    c.id,
    c.name,
    COUNT(o.id) as order_count
FROM analytics.customers c
LEFT JOIN raw.orders o ON c.id = o.customer_id
GROUP BY 1, 2;

Problems: - Manual execution order - No testing - No documentation - Hard to maintain

The dbt Way

models/staging/stg_customers.sql

select
    id as customer_id,
    name as customer_name,
    email,
    created_at
from {{ source('raw', 'customers') }}

models/marts/orders_summary.sql

select
    c.customer_id,
    c.customer_name,
    count(o.order_id) as order_count
from {{ ref('stg_customers') }} c
left join {{ ref('stg_orders') }} o
    on c.customer_id = o.customer_id
group by 1, 2

schema.yml

models:
  - name: orders_summary
    description: Customer order counts
    columns:
      - name: customer_id
        tests:
          - unique
          - not_null

Run with:

dbt run    # Executes all models in correct order
dbt test   # Runs all tests
dbt docs generate && dbt docs serve  # Generates documentation

Real-World Impact

Companies using dbt report:

• 80% faster analytics development
• 10x more data tests running
• 95% less time debugging data issues
• 100% version-controlled analytics code
• Automatic documentation for stakeholders

Getting Help

Throughout this tutorial, you'll find:

Best Practices

Industry-standard patterns from top data teams.

Common Pitfalls

Mistakes that trip up dbt beginners.

Code Examples

Real-world dbt code you can copy and adapt.

Deep Dives

Advanced topics for those who want to go further.

Ready to Transform Your Data?

Let's begin your journey into modern data engineering with dbt. Click "Next" below or navigate to Getting Started → Introduction to continue.

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Version: This tutorial covers dbt Core 1.7+ and dbt Cloud Last Updated: 2025 License: Open source and free to use

Quick Links

• Official dbt Documentation
• dbt Discourse Community
• dbt Slack Community
• dbt GitHub Repository