🚀 Getting Started with dbt Cloud: The Basics of Modern Data Transformation
If you’re new to dbt, you might have many questions: What is dbt? Why use it? How does it work? and How can I start transforming and managing data more easily? This article will walk you through the basics of dbt Cloud, the fully managed platform for dbt, helping you build a solid understanding and take your first steps in analytics engineering.
What is dbt Cloud?
dbt Cloud is a hosted, browser-based platform designed to simplify how modern data teams transform raw data into clean, reliable datasets ready for analysis.
It builds on the power of dbt (Data Build Tool) — an open-source framework for writing modular SQL transformations — and adds:
- A web-based IDE for writing and running models
- Seamless integration with Git for version control
- Automated job scheduling for running transformations on a cadence
- Visual data lineage and auto-generated documentation
- Built-in data testing and quality checks
- Team collaboration tools like access controls and alerts
With dbt Cloud, you can focus on writing transformation logic in SQL, while the platform manages the infrastructure, orchestration, and collaboration.
The Role of dbt in the Data Workflow
In a typical data pipeline, data flows through three stages:
Source Data → Transformations → Final Tables
dbt operates specifically in the Transformations stage. It helps you transform raw, often messy, data into clean, modeled tables that analysts and data scientists can trust.
How does this compare to working without dbt?
| Without dbt | With dbt Cloud |
|---|---|
| Raw data sits in your warehouse | Raw data sits in your warehouse |
| SQL queries are run manually in the warehouse | SQL transformations are written as modular dbt models |
| Transformations are run manually or with ad-hoc scripts | Automated and version-controlled transformation runs via dbt Cloud jobs |
| No built-in testing or lineage visualization | Built-in tests, documentation, and lineage visualization |
By organizing transformations in dbt, you gain modularity, visibility, and governance—key for scaling analytics reliably.
Getting Started: Building Models in dbt Cloud
What is a Model in dbt?
A model is a SQL file that defines a transformation step, which dbt compiles and runs to create tables or views in your data warehouse.
In dbt Cloud, your project directory typically looks like this:
my_dbt_project/
├── models/
│ ├── staging/
│ ├── intermediate/
│ ├── final/
│ └── schema.yml # for tests & docs
├── macros/
├── tests/
├── seeds/
├── dbt_project.yml
You write your SQL models inside the folders (e.g., models/staging/my_model.sql), organizing transformations from raw data (staging) to refined datasets (final).
Core Concepts for Building Models
1. Sources
Define your raw data tables in .yml files to declare where data originates.
version: 2
sources:
- name: sales
tables:
- name: orders
- name: customers
Reference these in your models:
select * from {{ source('sales', 'customers') }}2. Refs
Reference other dbt models to build dependencies:
select * from {{ ref('purchases_value') }}3. Materialization
Decide how dbt stores the results: as tables, views, or incremental tables.
{{
config(materialized='table')
}}
select * from {{ source('sales', 'customers') }}Or set materialization globally in dbt_project.yml:
models:
my_dbt_project:
staging:
materialized: view
intermediate:
materialized: table
Understanding Data Lineage and DAG in dbt Cloud
dbt automatically generates a Directed Acyclic Graph (DAG) that visually represents dependencies between your models. This lineage helps you understand how data flows:
- Upstream models: Data your model depends on
- Downstream models: Models that depend on your model
You can explore this lineage interactively in the dbt Cloud UI, which is crucial for debugging and impact analysis.
Writing and Running Tests in dbt Cloud
Quality is key in data transformation. dbt lets you define tests to validate assumptions like uniqueness, non-null values, and accepted values right alongside your models.
Example .yml tests:
models:
- name: orders
columns:
- name: order_id
tests:
- unique
- not_null
- name: status
tests:
- accepted_values:
values: ['active', 'inactive']Run your tests with:
dbt testTests run after models build; if tests fail, dbt flags errors but still creates the model. Using dbt build runs both models and tests, stopping downstream models if a test fails — ensuring quality gates.
Documenting Your Data Models
dbt encourages documenting models inline via .yml files, keeping docs close to code:
models:
- name: orders
description: "Contains order details with customer and payment info."
columns:
- name: order_id
description: "Unique identifier for each order."
- name: status
description: "Current status of the order."Generate and view docs with:
dbt docs generate
dbt docs serveThis documentation includes lineage and is always synced with your latest models, improving transparency and collaboration.
Version Control & Collaboration
dbt Cloud integrates tightly with Git, so your SQL models and configuration files live in version control—enabling collaboration, code reviews, and history tracking.
Typical Git workflow:
git checkout -b feature/add-customer-model
git add models/customers.sql
git commit -m "Add customers model"
git push origin feature/add-customer-modeldbt Cloud’s integration helps you link Git branches to environments, run jobs on pull requests, and ensure code quality with CI/CD workflows.
Wrapping Up: Why Start with dbt Cloud?
dbt Cloud empowers you to build clean, tested, documented, and version-controlled data models in a collaborative cloud environment. You don’t need to worry about managing infrastructure or orchestration—focus purely on transforming your data with confidence.
If you’re ready to make your data pipeline more scalable and maintainable, dbt Cloud is the perfect place to start your analytics engineering journey.
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