dbt
Transforming raw data inside a warehouse using SQL and version-controlled data models
dbt (data build tool) is an open-source data transformation framework that lets analytics engineers write SQL SELECT statements to define how raw source data should be transformed into clean, modelled tables inside a data warehouse. Each model is a SQL file; dbt handles the dependency resolution, materialisation strategy, and execution order, replacing manual ETL scripting with a version-controlled, testable workflow. Built-in tests validate assumptions about data — checking for nulls, uniqueness, referential integrity — and documentation is generated automatically from model metadata. dbt works with Snowflake, BigQuery, Redshift, PostgreSQL, and other major warehouses. It has become the standard tool for the analytics engineering discipline, sitting at the intersection of data engineering and analytics.
dbt Use Cases
- Defining data transformation logic in modular SQL files that build on one another in a dependency-managed execution graph
- Testing data quality assumptions automatically on each run, flagging nulls, duplicate keys, and broken referential relationships
- Documenting data models and column-level definitions that generate a browsable data catalogue from the same codebase
- Running incremental models that process only new or changed records, reducing warehouse compute costs on large datasets
- Refactoring messy analytics SQL into reusable staging, intermediate, and mart layers that reflect agreed data modelling conventions
dbt Learning Resources
The official dbt documentation at docs.getdbt.com covers project structure, models, tests, and deployment in detail. dbt Learn at courses.getdbt.com offers free self-paced courses for analysts building their first dbt project from scratch.