Can you explain DBT in technical language?

DBT, or Data Build Tool, is a transformation framework that enables data analysts and engineers to build, test, and document data pipelines using SQL. It treats each SQL file as a model that can be compiled into a materialized table or view in a data warehouse such as Snowflake, BigQuery, or Redshift. DBT manages dependencies between models automatically; when a source table changes, only downstream models that depend on it are recompiled. It supports incremental models, allowing only new or changed rows to be processed, which improves performance. DBT also integrates Jinja templating for reusable logic, provides a testing framework to assert data quality, and generates documentation and lineage graphs. Projects are version‑controlled, making CI/CD straightforward. In practice, a DBT run compiles all models, executes them in the correct order, and logs the results, giving a reproducible, auditable transformation layer on top of raw data.