dbt (data build tool)
A transformation tool that enables analytics engineers to transform data using SQL, with software engineering best practices.
In-Depth Explanation
dbt (data build tool) is a transformation workflow tool that enables analytics engineers to transform data in their warehouses using SQL. It brings software engineering practices like version control, testing, and documentation to analytics.
dbt concepts:
- Models: SQL files defining transformations
- Tests: Data quality assertions
- Documentation: Auto-generated docs
- Sources: Raw data references
- Seeds: CSV files loaded to warehouse
- Snapshots: Historical data tracking
dbt features:
- SQL-based transformations
- Dependency management
- Incremental processing
- Testing framework
- Documentation generation
- Jinja templating
dbt deployment:
- dbt Core: Open source, CLI
- dbt Cloud: Managed platform
- CI/CD integration
- Scheduling and orchestration
Business Context
dbt has become the standard for analytics transformation, enabling reliable, tested, documented data pipelines that analysts can understand and maintain.
How Clever Ops Uses This
We use dbt in AI data pipelines for Australian businesses, ensuring data quality and enabling reproducible transformations that feed ML models.
Example Use Case
"Building dbt models that transform raw sales data into clean, tested, documented datasets that power both dashboards and ML-based demand forecasting."