Data transformation · dbt Labs

dbt

Transforms data already in the warehouse with SQL SELECT statements, adding version control, testing, documentation and lineage.

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dbt is the transformation step of the ELT pattern: data is loaded raw into the warehouse, then dbt turns SQL SELECT statements into tables and views inside it. Each model is a file in version control; dbt works out the dependency graph between models, runs them in order, and can test the results and generate documentation and lineage from the same definitions. It pushes all computation down to the warehouse rather than processing data itself. dbt Core is the free, open-source command-line tool; the dbt platform adds hosted scheduling, an IDE, a semantic layer, and governance features on top of it. Its conventions — models, refs, tests, sources — became the default way analytics engineers structure warehouse SQL, which is why most competing tools position themselves against it.

At a glance

Vendor dbt Labs
Pricing model Open source + paid options
Free tier Yes
Deployment Cloud, Self-hosted
Open source Yes (Apache-2.0)
Best for Analytics engineers turning raw warehouse tables into tested, documented models the whole company queries.

Pricing

dbt Core is free and open source; the hosted platform is free for one developer, then priced per user with custom pricing above that.

Plan Price Notes
Developer Free For individual developers getting started with dbt, without managing infrastructure
Starter $100 per user/month For teams collaborating on their first dbt project
Enterprise Custom pricing For organizations scaling data collaboration and governance
Enterprise+ Custom pricing For complex deployments requiring advanced security and control

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • SQL models compiled and run in dependency order
  • Data tests on columns and relationships
  • Generated documentation and column-level lineage
  • Jinja templating and reusable macros
  • Incremental models for large tables
  • Package manager for shared code
  • Semantic layer for shared metric definitions (platform)

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

SQLMesh Open-source SQL transformation framework with column-level lineage and virtual data environments, built as a drop-in dbt alternative. Dataform Google Cloud's built-in SQL transformation tool for BigQuery, offering dbt-like modeling without a separate platform to run. Coalesce GUI-driven Snowflake transformation tool that generates and version-controls SQL, aimed at teams who want dbt's rigor with a visual editor.

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Terms to know

dbt (data build tool) An open-source tool from dbt Labs that lets analytics teams write data transformations as version-controlled, tested SQL. ELT (extract, load, transform) A data integration pattern that loads raw data first and transforms it afterward using the warehouse's own compute. Data modeling The practice of organizing data into entities, relationships and schemas that make it accurate and efficient to query. Semantic layer A layer that defines business metrics and dimensions once, centrally, so every tool and report uses the same numbers. Analytics engineer A role that builds and maintains the transformed, tested data models analysts and dashboards rely on.

Related guides

Analyst vs analytics engineer vs data engineer vs data scientist What each of the four core data roles actually does day to day, where they overlap, and how careers move between them. How to build a metrics layer Define each metric once, in code, so every tool that queries revenue or churn gets the same number — and govern changes to it. How to build a modern data stack Ingestion, warehouse, transformation, BI and activation — what a data stack needs at each company stage, and what to defer. How to choose a data transformation tool Choosing between dbt and its rivals comes down to editor vs code, warehouse lock-in, rebuild cost and who is checking the SQL before it ships. How to decide build vs buy for analytics A framework for the build-vs-buy decision in analytics infrastructure — total cost, maintenance burden and lock-in, layer by layer. How to reduce data warehouse costs Where usage-based warehouse spend actually goes, and the levers — query, storage, scheduling — that reliably bring it down.