data-quality-testing

Generate comprehensive dbt tests by creating YAML and SQL test definitions across staging, marts, and sources. This includes schema tests, unit tests, custom SQL checks, and anomaly monitoring to identify data quality gaps in production pipelines.

Yes, the dbt testing approach supports integration with Elementary and dbt_utils. It generates dbt_utils-based validations and Elementary anomaly tests for production monitoring, alongside standard schema tests.

Audit existing test coverage by evaluating schema tests, custom SQL assertions, and anomaly tests across sources, staging, and marts. This identifies missing validations and helps implement comprehensive checks to close data quality gaps.

Yes, dbt 1.8+ data_tests are fully supported for schema and unit testing. The test definitions satisfy requirements for dbt 1.8+ integration, including generic schema tests and custom SQL assertions.

Custom SQL checks and anomaly monitoring are needed for production pipelines requiring advanced data quality validation. They catch data quality issues before they impact dashboards by testing staging, marts, and sources with custom assertions.

Schema tests for dbt data quality should include unique, not_null, and relationships tests for every model. Additionally, dbt_utils-based validations like accepted_range, expression_is_true, and coverage provide comprehensive coverage.