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dbt

In-depth entry

by dbt Labs (Fivetran + dbt Labs) · Framework · also known as dbt Core, data build tool, dbt Fusion engine, dbt platform (dbt Cloud)

SQL-first transformation framework that compiles templated models, tests and documentation into warehouse tables; open-source Core plus a commercial platform

Apache-2.0 licenseOpen sourceFreemiumPaidEnterpriseLocalSelf-hostedCloudSelf-hostable
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Link verified Nov 2, 2026

Overview

dbt (data build tool) is a transformation framework from dbt Labs in which analysts write SELECT statements as versioned models, and the tool compiles them with Jinja templating, resolves dependencies into a DAG, materializes tables, views or incremental tables in the target warehouse, and runs schema and data tests alongside generated documentation and lineage. It performs the T of ELT: data already loaded into Snowflake, BigQuery, Databricks, Redshift, PostgreSQL, DuckDB or ClickHouse is reshaped in place through adapters, and dbt itself holds no data.

dbt Core is the open-source command-line tool under Apache 2.0. In 2025 dbt Labs introduced the Rust-based dbt Fusion engine, and in June 2026 announced dbt Core v2 as an alpha built on the same open-sourced foundations, with the dbt-core repository (now github.com/dbt-labs/dbt) as the canonical home and the Fusion binary distributed free with some proprietary components. The company completed its merger with Fivetran on 1 June 2026. The commercial dbt platform (formerly dbt Cloud) adds a hosted IDE, scheduling, CI, a semantic layer and the Catalog, with free developer, team and enterprise plans.

dbt is the de facto standard for modeling layers in analytics engineering and pairs naturally with Airflow, Dagster or Prefect for orchestration and with DuckDB for local development. Limitations: it is SQL-centric (Python models exist only on some warehouses), it does not move data, large projects can have long parse and run times on v1, and the fast-moving Core v1/v2 and Fusion transition means teams must track which engine and adapter versions they run.

Editorial note on uncertainty. Fusion engine licensing for its proprietary components was not reviewed in detail; license_spdx applies to dbt Core.

Capabilities

  • SQL models with Jinja templating and ref() dependency resolution
  • Materializations: table, view, incremental, snapshot, seed
  • Schema and data tests, unit tests and source freshness checks
  • Generated documentation and lineage graph
  • Adapters for Snowflake, BigQuery, Databricks, Redshift, PostgreSQL, DuckDB, ClickHouse and more
  • Packages, macros and semantic-layer metrics
  • Hosted scheduling, IDE and CI in the dbt platform

Typical workflows

  1. Model a warehouse from raw loaded tables to marts
  2. Local development against DuckDB, then deploy to a warehouse
  3. Schedule dbt runs from Airflow, Dagster or Prefect
  4. Enforce data tests in CI before merging model changes

Strengths

  • Software-engineering practices (version control, tests, docs) for SQL
  • Large community, packages and adapter ecosystem
  • Apache 2.0 Core runs anywhere
  • Runs on the warehouse's own compute

Limitations

  • Transformation only; extraction and loading need other tools
  • Primarily SQL; Python models limited to certain platforms
  • Core v1 to v2 and Fusion transition in progress
  • Advanced collaboration features are in the paid platform

Prerequisites

  • A supported warehouse or database and credentials
  • Python for dbt Core v1, or the Fusion/Core v2 binary

Who it is for

  • Analytics engineers
  • Data teams building warehouse models

Alternatives & comparison

Airflow, Dagster and Prefect are orchestrators that schedule and monitor pipelines, including dbt runs; dbt itself only defines and executes transformations inside the warehouse. Spark is a general compute engine that can perform the same transformations in code rather than templated SQL. DuckDB is a frequent local target for dbt projects. Dagster in particular models dbt assets natively.

  • Apache Airflow

    Python workflow orchestrator for scheduling and monitoring DAGs of tasks, with hundreds of provider packages and managed offerings from several clouds

    Open sourceOpen source
  • Apache Spark

    Distributed data processing engine with DataFrame and SQL APIs in Python, Scala, Java and R, plus structured streaming and MLlib

    Open sourceOpen source
  • Dagster

    Asset-oriented Python orchestrator that models pipelines as software-defined data assets with lineage, checks and a hosted Dagster+ option

    Open sourceOpen source
  • DuckDB

    In-process analytical SQL database that queries Parquet, CSV and Arrow data directly from Python, R, Node or the CLI

    Open sourceOpen source
  • Great Expectations (GX Core)

    Python data-quality framework that declares Expectations about datasets, validates them against pandas, Spark or SQL sources and produces Data Docs

    Open sourceOpen source
  • Prefect

    Python workflow orchestration library and server where decorated functions become observable, retryable flows, with Prefect Cloud as hosted option

    Open sourceOpen source
Compare dbt with its alternatives →

Sources & verification

Primary sources consulted for this record. Claims are tied to the fields each source supports; the automated link checker re-verifies every URL on a schedule.

  • License
    dbt LICENSE (Apache 2.0)

    https://github.com/dbt-labs/dbt/blob/main/LICENSE

    Supports: license_spdx, source_model

    github.com/dbt-labs/dbt-core redirects to this repository

    checked Nov 1, 2026
  • News
    Fivetran and dbt Labs complete merger (press release)

    https://www.fivetran.com/press/fivetran-dbt-labs-complete-merger-to-create-the-data-infrastructure-for-trusted-ai-agents

    Supports: vendor

    checked Nov 1, 2026
  • Official documentation
    dbt Developer Hub

    https://docs.getdbt.com/

    Supports: capabilities, integrations

    checked Nov 1, 2026
  • Official site
    dbt Labs

    https://www.getdbt.com/

    Supports: name, summary, vendor

    checked Nov 1, 2026
  • Pricing page
    dbt pricing

    https://www.getdbt.com/pricing

    Supports: pricing_model

    checked Nov 1, 2026
  • Release notes
    dbt Core v2 is here (June 2026)

    https://docs.getdbt.com/blog/dbt-core-v2-is-here

    Supports: product_status, license_spdx, repo_url, vendor

    States Core v2 is Apache 2.0 in the dbt-core repository and refers to the combined Fivetran + dbt Labs team

    checked Nov 1, 2026
Confidence
High
Reviewed
Nov 1, 2026
Links
Verified · Nov 2, 2026

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