dbt
data build tool
Judgement: Adopt
Analytics-engineering default: SQL-first models, tests, lineage.
SQL-first transformation framework with built-in tests, documentation, and lineage. The de-facto standard for analytics engineering — used both to model warehouse data and to express the quality checks that ride alongside the transformations.
Vendor-governed by dbt Labs rather than a foundation, so it isn't an open standard in the strict sense. Included here on the de-facto criterion: its YAML and Jinja-SQL conventions are what other tools (SQLMesh, Lightdash) interoperate with.
Why it counts as a standard
The dbt project artefacts — the manifest.json, the model YAML, the schema.yml tests — have become the interchange format for analytics engineering. SQLMesh, Lightdash, Elementary, dbt-osmosis and many other tools read or emit dbt's project structure rather than inventing their own. Quality checks declared in dbt are part of that surface. That makes the format itself the standard, even though dbt Labs governs it.
At a glance
- Category
- Processing
- Governance
- dbt Labs (vendor-driven open source)
- Status
- De-facto standard for analytics engineering; vendor-governed format
- First released
- 2016
Links
Related standards
Other standards in Processing.
- Pandas — pandas
- Spark — Apache Spark
- SQL DML — SQL Data Manipulation Language
- Beam — Apache Beam
- Ibis
- XSLT — Extensible Stylesheet Language Transformations
See dbt in context
Open the interactive Data Landscape to compare dbt against every other open standard, or grab the raw JSON. Spotted something wrong? Open an issue.