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What is dbt?

dbt transforms raw warehouse data into trusted data products. You write simple SQL select statements, and dbt handles the heavy lifting by creating modular, maintainable data models that power analytics, operations, and AI -- replacing the need for complex and fragile transformation code.

dbt is the industry standard for data transformation, helping you get more work done while producing higher quality results. You can use dbt and its framework to:

  • Centralize and modularize your analytics code, while also providing your data team with guardrails typically found in software engineering workflows.
  • Collaborate on data models to safely deploy and monitor data transformations in production.
  • Apply software engineering best practices like version control, testing, modularity, CI/CD, and documentation to analytics workflows.

Backed by a 100,000+ member community, dbt helps teams build high-quality, trustworthy data pipelines faster.

dbt works alongside your ingestion, visualization, and other data tools, so you can transform data directly in your cloud data platform.dbt works alongside your ingestion, visualization, and other data tools, so you can transform data directly in your cloud data platform.

Read more about why we want to enable analysts to work more like software engineers in The dbt Viewpoint. Learn how other data practitioners around the world are using dbt by joining the dbt Community.

dbt framework​

Use the dbt framework to quickly and collaboratively transform data and deploy analytics code following software engineering best practices like version control, modularity, portability, CI/CD, and documentation. This means anyone on the data team familiar with SQL can safely contribute to production-grade data pipelines.

The dbt framework is composed of a language and an engine:

  • The dbt language is the code you write in your dbt project — SQL select statements, Jinja templating, YAML configs, tests, and more. It's the standard for the data industry and the foundation of the dbt framework.

  • The dbt engine compiles your project, executes your transformation graph, and produces metadata. dbt supports two engines which you can use depending on your needs:

    • The dbt Core engine, which renders Jinja and runs your models.
    • The dbt Fusion engine, which goes beyond Jinja rendering to statically analyze your SQL — validating syntax and logic before your SQL is sent to the database (saving compute resources), and supports LSP features.

dbt Fusion engine​

The dbt Fusion engine is a Rust-based engine that delivers a lightning-fast development experience, intelligent cost savings, and improved governance. Fusion understands SQL natively across multiple dialects, catches errors instantly, and optimizes how your models are built — bringing SQL comprehension and state awareness, instant feedback, LSP, and more to every dbt workflow.

Fusion powers dbt in the dbt platform, VS Code / Cursor, and locally from the command line. You don't need to have a dbt platform project to use the dbt Fusion engine.

For more information, refer to About the dbt Fusion engine, supported features, and the get started with Fusion pages.

dbt Core engine​

dbt Core is the open-source, Python-based engine that enables data practitioners to transform data. dbt Core surfaces feedback when you run or build your project. It doesn't include Fusion features like the LSP, for example, which provides instant feedback as you type.

Learn more with the quickstart for dbt Core.

How to use dbt​

You can deploy dbt projects in different ways depending on your needs:

All ways support using the dbt Fusion engine or dbt Core engine.

dbt platform​

The dbt platform offers the fastest, most reliable, and scalable way to deploy dbt. It can be powered by the dbt Fusion engine or dbt Core engine, and provides a fully managed service with scheduling, CI/CD, documentation hosting, monitoring, development, and alerting through a web-based user interface (UI).

The dbt platform offers multiple ways to develop and collaborate on dbt projects:

Learn more about the dbt platform features and try one of the dbt Quickstarts.

You can learn about plans and pricing on www.getdbt.com.

dbt local development​

Use the dbt framework and develop dbt projects from your command line or code editor:

  • Install the dbt VS Code extension — Combines the dbt Fusion engine performance with visual features like autocomplete, inline errors, and lineage. Includes LSP features and suitable for users with dbt platform projects or running dbt locally without a dbt platform project. Recommended for local development.
  • Install the Fusion CLI — The dbt Fusion engine from the command line, but doesn't include LSP features.
  • Install the dbt CLI — The dbt platform CLI, which allows you to run dbt commands against your dbt platform development environment from your local command line.
  • Install dbt Core — The open-source, Python-based CLI that uses the dbt Core engine. Doesn't include LSP features.

Why use dbt​

As a dbt user, your main focus will be on writing models (select queries) that reflect core business logic – there's no need to write boilerplate code to create tables and views, or to define the order of execution of your models. Instead, dbt handles turning these models into objects in your warehouse for you

  • No boilerplate — Write business logic with just a SQL select statement or a Python DataFrame. dbt handles materialization, transactions, DDL, and schema changes.
  • Modular and reusable — Build data models that can be referenced in subsequent work. Change a model once and the change propagates to all its dependencies, so you can publish canonical business logic without reimplementing it.
  • Fast builds — Use incremental models and leverage metadata to optimize long-running models.
  • Tested and documented — Write data quality tests on your underlying data and auto-generate documentation alongside your code.
  • Software engineering workflows — Version control, branching, pull requests, CI/CD, and package management for your data pipelines. Write DRYer code with macros and hooks.
  • State-aware orchestration — Use the dbt Fusion engine to orchestrate your dbt projects and models with state-awareness orchestration, which automatically determines which models to build by detecting changes in code or data. This reduces runtime and costs by only building the models that have changed.

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