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In enterprise data platforms, ensuring high-quality, trusted data in the Golden Layer is critical. In this post, we will showcase a powerful and production-ready pattern that combines vanilla DBT tests, custom macro logic, automated audit trail, and real-time Snowflake alerts, all leading to a clean and trusted Golden Semantic Model—without writing a single line of Python. This framework combines the strengths of DBT testing, Snowflake automation, and semantic layer to build a reliable Golden Layer pipeline with proactive quality checks and notifications.
Here are the key innovations that make this pipeline a standout in the DBT + Snowflake ecosystem:
1.Custom macro test and vanilla tests defined together in a single YAML file
2. Failed records are automatically stored in Snowflake audit tables for transparency
3. A separate runtime model triggers email alerts dynamically via Snowflake’s native SYSTEM$SEND_EMAIL
4. Semantic model automatically excludes erroneous records, ensuring trusted outputs for business
5. Entire workflow is driven by DBT macros and SQL—no external orchestration needed.
Technical Details:
1. Golden Model: Blending Vanilla + Custom Tests (USP #1)
We start with a unified Golden_Aggregate.sql model that combines customer, order, and lineitem data. In the accompanying golden_customer_orders.yml, we demonstrate how vanilla tests and custom tests can live together—streamlining your test logic in one place.
2. Custom Macro Test: Catching Business Logic Violations (USP #2)
Business logic can’t always be enforce with vanilla rules. That’s where this custom test macro shines.
Purpose: Detect orders with unusually high revenue that were not marked as RETURNED, a key anomaly in our business.
3. Automated Audit Table: No More Guessing Failures (USP #3)
Using store_failures: true, both vanilla and custom test failures are automatically written to Snowflake audit tables:
data_load_dbt.dbt_test__audit.net_revenue_negative_when_returned_Golden_Aggregate_NET_REVENUE
This makes your failed records queryable, reviewable, and traceable.
4. Runtime Model: Email Stakeholders When Issues Detected (USP #4)
Now here’s the game-changer: a dedicated semantic model, golden_aggregate_clean.sql, dynamically queries the audit table, and if failures exist—calls a macro to send an email via Snowflake!
Supporting Macro
Validation in Snowflake:
Cleansed Records in Golden Model:
Alerts via DBT:
Conclusion:
This end-to-end framework demonstrates how DBT and Snowflake can work in perfect harmony to enforce data quality, capture failures, alert stakeholders, and deliver only trusted records downstream. With minimal code and maximum control, we’ve created a self-healing Golden Model pipeline—one that every modern data team can adopt and scale
