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dbt enables analytics teams to transform data within data warehouses by integrating SQL into a structured workflow. It promotes collaboration and automation to derive high-quality data.
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Usable application software: supports people in a task.
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dbt is a development tool for analytical data transformations in a warehouse or lakehouse. Teams write SQL models, tests, and documentation as a versioned software project.
dbt emerged in 2016 from analytics work around RJMetrics and the company that became Fishtown Analytics, now dbt Labs. Chris Merrick wrote the first lines of code; Tristan Handy, Drew Banin, and the early team developed it into a tool for more reliable, collaboratively maintained SQL transformations.
Think of dbt as a build system for data that already lives in the warehouse. For every desired table or view, the team writes a SQL SELECT query as a model. A model uses `ref()` to point to other models, allowing dbt to discover dependencies and the correct execution order. During a run, dbt creates the tables or views directly in the warehouse. Tests then check important data assumptions, while documentation and lineage show how each result was produced.
Modular SQL SELECT queries describe which analytical tables or views should be created in the warehouse.
References connect models; dbt derives execution order and the impact of changes from those connections.
Configuration determines whether a model is built as a view, table, incremental model, or another supported form.
Declarative checks validate data assumptions; documentation makes models, relationships, and ownership understandable.
dbt fits teams that want to develop analytical transformations transparently, repeatedly, and collaboratively. It does not ingest raw data: source data must already be available in the warehouse or lakehouse. Runtime cost, incremental logic, data modeling, and clear data ownership remain deliberate design concerns.
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