data-quality

Data quality detection in dbt uses schema tests and custom SQL tests to catch missing values, duplicates, and inconsistencies. dbt tests run validations against your transformed data, then Great Expectations provides additional dataset validation. Combined, they identify issues before data reaches reporting.

Yes. Great Expectations integrates with dbt workflows to validate datasets beyond schema constraints. While dbt tests handle schema and custom SQL validations, Great Expectations adds flexible dataset profiling and anomaly detection, enabling comprehensive quality coverage across your pipeline.

Monitor row counts, null rates, schema drift, and freshness SLAs to catch pipeline failures early. These metrics reveal missing data, unexpected structural changes, and delays in data arrival. Dashboards tracking these dimensions provide ongoing confidence in data quality throughout your ETL workflow.

Create monitoring dashboards that visualize row counts, null rates, schema changes, and freshness SLAs from your dbt tests and Great Expectations validations. These dashboards aggregate quality metrics into a single view, enabling quick detection of anomalies and pipeline degradation.

dbt tests validate schema structure and run custom SQL assertions within your transformation DAG. Great Expectations provides broader dataset validation, profiling, and statistical anomaly detection. Together they create layered quality checks: dbt for structural guarantees, Great Expectations for behavioral validation.

You can use either alone, but they complement each other. dbt tests are lightweight and built into your transformation logic. Great Expectations excels at complex validations and drift detection. Combined, they satisfy functional requirements for schema tests, custom validations, and continuous monitoring without gaps.