dbt
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What is dbt
dbt (data build tool) is an analytics engineering framework for transforming data in a warehouse or lakehouse using SQL and version-controlled code. It is used by data teams to build, test, document, and deploy modular data models that support reporting, analytics, and downstream applications. dbt emphasizes software engineering practices (Git workflows, CI/CD, code review) and runs transformations inside the target data platform rather than moving data out to an external engine.
SQL-first transformation workflow
dbt lets teams express transformations primarily in SQL with Jinja templating, which fits common analytics engineering skill sets. It compiles models into warehouse-native SQL and executes them in the target platform, reducing the need to operate separate compute for transformations. This approach aligns well with modern cloud data warehouses and lakehouse SQL engines used for large-scale analytics.
Strong engineering governance
dbt projects are code-based and work naturally with Git, pull requests, and CI pipelines. Built-in testing (e.g., schema and custom tests), documentation generation, and lineage graphs support reviewability and operational discipline. These capabilities help standardize how teams manage changes to shared analytical datasets across multiple contributors.
Large ecosystem and integrations
dbt has a broad adapter ecosystem for common warehouses and SQL engines, plus integrations with orchestration, catalog, and observability tools. The package ecosystem (dbt packages) provides reusable macros and models for common patterns. This reduces time to implement standardized transformations and encourages consistent conventions across projects.
Not a full ETL stack
dbt focuses on in-warehouse transformations (the “T” in ELT) and does not natively handle source extraction or bulk loading. Teams typically pair it with separate ingestion tools and an orchestrator to manage end-to-end pipelines. This increases the number of components to operate compared with more all-in-one data integration platforms.
Limited non-SQL data prep
dbt is optimized for set-based SQL transformations and is less suited to complex non-SQL preparation tasks such as heavy Python-based feature engineering, unstructured data processing, or advanced data science workflows. While extensions exist, these use cases often require additional platforms or custom code outside dbt. Organizations with many non-SQL transformations may find the workflow fragmented.
Semantic layer is optional
dbt’s semantic layer capabilities are not the core of the open-source workflow and may require additional configuration and compatible BI/consumption tooling. Metric definitions and governance can still become duplicated across tools if teams do not standardize on a single approach. Companies seeking a centralized, tool-agnostic semantic layer may need complementary products or stricter governance processes.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Developer | Free | 14-day free trial of Starter plan; One Developer seat; 3,000 successful models built per month; 1 project; Browser-based IDE; MFA; Job scheduling; Keeps on latest dbt release. |
| Starter | $100 per user/month | Five (5) developer seats included; 15,000 successful models built per month; 5,000 queried metrics per month; 1 project; Includes dbt Catalog basic, dbt Semantic Layer basic, dbt Copilot code generation, API access. |
| Enterprise | Custom pricing | Custom Developer seat count; 100,000 successful models built per month; 20,000 queried metrics per month; 30 projects; Includes dbt Catalog advanced, dbt Semantic Layer advanced, dbt Copilot, dbt Canvas, dbt Insights, cost optimization features, dbt Mesh; Analyst seat option available. |
| Enterprise+ | Custom pricing | Custom Developer seat count; 100,000 successful models built per month; 20,000 queried metrics per month; Unlimited projects; Adds PrivateLink, IP Restrictions, Rollback, Hybrid projects; Maximum control over security and deployment. |
Seller details
dbt Labs, Inc.
Philadelphia, PA, USA
2016
Private
https://www.getdbt.com/
https://x.com/dbt_labs
https://www.linkedin.com/company/dbt-labs/
