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dbt

Transform data in your warehouse with SQL + software engineering best practices.

0Open Source
ELT & Transform
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Overview

dbt (data build tool) enables analytics engineers to transform data in their warehouses using SQL and software engineering best practices — version control, testing, documentation, and modular models.

Key Features

  • SQL-first transformation with Jinja templating for DRY models
  • Built-in testing framework for data quality assertions
  • Auto-generated documentation with lineage DAG visualization
  • dbt Cloud for managed scheduling, CI/CD, and IDE
  • Packages for 100+ reusable model libraries (dbt-utils, dbt-expectations)
  • Supports Snowflake, BigQuery, Redshift, Databricks, DuckDB, and more

Real-World Workflows

Build a modular analytics data model

  1. 1Organize SQL models into staging, intermediate, and mart layers
  2. 2Use ref() to define dependencies between models
  3. 3Add schema.yml tests (unique, not_null, accepted_values) per column
  4. 4Run dbt build to compile, run, and test all models

CI/CD for data transformations

  1. 1Set up a dbt Cloud job triggered on PR merge
  2. 2Run dbt test on each PR to catch regressions before production
  3. 3Use slim CI to run only modified models with state comparison
  4. 4Publish auto-generated docs to a static site on merge

Getting Started

# Install dbt with Snowflake adapter
pip install dbt-snowflake

# Initialize a new project
dbt init my_project
cd my_project

# Run models against the warehouse
dbt run

# Run tests
dbt test

# Generate and serve docs
dbt docs generate && dbt docs serve

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