dbt (data build tool): Modern Data Transformation Explained

Galaxy Glossary

What is dbt and why should data teams use it?

dbt is an open-source framework that lets data teams transform, test, and document data in the warehouse using version-controlled SQL.

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Description

Table of Contents

dbt, short for data build tool, has rapidly become the de-facto standard for transforming raw warehouse tables into reliable, analytics-ready datasets. By treating transformations as software—complete with modular SQL, version control, testing, and documentation—dbt enables analytics engineering practices that dramatically improve data quality and team productivity.

Definition

dbt is an open-source command-line tool (pip install dbt-core) and ecosystem that lets you write modular SQL models, run dependency-aware builds, add tests, auto-generate documentation, and maintain everything in Git. You think in SQL, dbt handles dependency graphs, execution order, and metadata.

Why dbt Matters

As companies move analytics to cloud data warehouses (Snowflake, BigQuery, Redshift, Databricks, etc.), they need a scalable way to convert raw ingested data into clean, governed tables. dbt solves this by:

  • Applying software-engineering rigor (version control, CI/CD, code review) to analytics work.
  • Providing declarative dependencies so models build in the right order.
  • Making testing a first-class citizen to catch issues sooner.
  • Generating a data catalog from the same codebase, reducing documentation drift.
  • Lowering the barrier for analysts to adopt engineering best practices—everything stays in SQL.

How dbt Works

The core idea is simple: each SQL file in your project is a model that selects from upstream sources and models. When you run dbt run, dbt materializes those SELECT statements as views or tables in your warehouse, honoring the dependency graph.

Models, Sources, and Seeds

Models live under models/ and define transformations. Sources declare raw tables, giving them rich metadata and enabling freshness checks. Seeds are CSV files you want loaded as reference tables.

Testing and Documentation

Add YAML blocks under tests: for schema tests (e.g., not_null, unique) or write custom SQL tests. dbt Docs then compiles a website where each model shows SQL lineage, tests, and descriptions.

Practical Example: Building an Orders Model

Suppose you ingest raw e-commerce data into raw.shopify_orders. You might create:

  • stg_orders.sql – standardizes column names
  • dim_customers.sql – aggregates customer info
  • fct_orders.sql – fact table joining orders to customers

Because fct_orders references stg_orders and dim_customers, dbt automatically determines the correct build order.

Best Practices

1. Follow the “staging → mart” pattern

Keep raw logic in stg_* models, business logic in dim_*/fct_* marts. This separation simplifies debugging and change management.

2. Test early, test often

Add not_null, unique, and conditional tests in staging models so bad data never reaches downstream dashboards.

3. Use CI/CD

Set up GitHub Actions to run dbt build --select modified+ on pull requests. You’ll prevent bad SQL from merging to main.

4. Document as you go

Description blocks power dbt Docs and help new teammates onboard quickly.

Common Mistakes and How to Avoid Them

  1. Skipping tests. Relying on manual QA leads to silent data corruption. Add schema and data tests for every new model.
  2. Letting dependency graphs sprawl. Over-nesting models can slow builds. Periodically refactor or use dbts exposures to clarify lineage.
  3. Hard-coding environment-specific logic. Use var() and env_var() so models deploy cleanly across dev, staging, prod.

Working Code Example

-- models/stg_orders.sql
with raw as (
select *
from {{ source('shopify', 'orders') }}
)

select
id as order_id,
cast(customer_id as int) as customer_id,
cast(subtotal_price as numeric) as subtotal_amount,
cast(created_at as timestamp) as created_at
from raw;

Run it with dbt run --select stg_orders. dbt renders Jinja, resolves {{ source }}, and creates stg_orders as a view or table.

FAQ

Is dbt a replacement for ETL tools?

Not exactly. dbt focuses on the T (transform) once data already lives in your warehouse. Youll still need an EL tool (Fivetran, Airbyte, custom pipelines) to load raw data.

How does dbt compare to traditional stored procedures?

Stored procedures mix logic and execution. dbt externalizes logic into version-controlled SQL files, adds testing, and shows lineage—making it easier to collaborate and audit.

Can I edit dbt models in Galaxy?

Yes. Because dbt models are plain SQL, you can use Galaxys lightning-fast editor and AI copilot to write, refactor, and share dbt SQL files before running dbt locally or in CI.

Do I need dbt Cloud?

dbt Core is free and open source. dbt Cloud adds a managed scheduler, IDE, and governance. Many teams start with Core and move to Cloud as needs grow.

Why dbt (data build tool): Modern Data Transformation Explained is important

Without a structured transformation layer, data warehouses turn into junk drawers of half-clean tables, eroding trust in analytics. dbt introduces version control, testing, and documentation so data products stay accurate and reproducible—critical for any organization that relies on data-driven decisions.

dbt (data build tool): Modern Data Transformation Explained Example Usage


-- Query an analytics-ready table built by dbt
SELECT customer_id, SUM(total_amount) AS lifetime_value
FROM analytics.fct_orders
GROUP BY customer_id
ORDER BY lifetime_value DESC
LIMIT 25;

dbt (data build tool): Modern Data Transformation Explained Syntax



Common Mistakes

Frequently Asked Questions (FAQs)

Is dbt a replacement for ETL tools?

Not entirely. It handles transformations after data lands in your warehouse; you still need EL tooling for extraction and loading.

How does dbt differ from writing SQL in spreadsheets or notebooks?

dbt enforces modularity, version control, testing, and documentation, turning ad-hoc SQL into maintainable code.

Can I use Galaxy to edit dbt SQL files?

Absolutely. Galaxys IDE-style SQL editor and AI copilot accelerate writing and refactoring dbt models in your local repo.

Do I need dbt Cloud for scheduling?

No. You can schedule dbt run in any orchestrator (Airflow, GitHub Actions). dbt Cloud simply provides a managed option.

Want to learn about other SQL terms?

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