dbt

A dbt model expresses a transformation that produces a relation or other supported output in the target platform. References between models form a dependency graph, allowing dbt to determine execution order and generate lineage information. Materialization choices determine whether results are stored as tables, views or incremental structures. Tests express expectations about the produced data, while documentation records meaning and ownership. dbt focuses on transformation rather than serving as a universal extraction engine or replacing the warehouse. Competence therefore includes SQL and data-model semantics as well as project configuration and an understanding of how the destination executes the generated work.

The practitioner defines sources and models, uses explicit references and selects materializations appropriate to update patterns. They add tests for keys, relationships and important business assumptions, then review generated queries and warehouse behavior. Useful artifacts include a documented transformation graph and an incremental model's recovery strategy. CI should evaluate changed models with representative data where feasible. The team also checks full refreshes, late corrections and deletion handling, because an incremental query that works on a new batch may still produce incorrect historical results.

Tests can miss semantic errors, and a well-organized graph can still encode incorrect joins or metric definitions. Incremental models add assumptions about change detection and recovery that need explicit validation. Destination-specific behavior also affects performance and correctness. dbt does not remove the need for source quality, orchestration or access governance. The strongest projects keep model meaning understandable and demonstrate how changes affect both current and historical outputs.