dbt-starrocks-testing

What problem does it solve?

This Skill helps you prevent broken or stale data from reaching downstream analytics by making dbt tests that are tailored for StarRocks workloads.

Core Features & Use Cases

• Generic dbt tests in schema.yml: Use standard tests like not_null, unique, accepted_values, and relationships, plus dbt-expectations assertions for numeric and typing checks.
• Source freshness checks: Configure loaded_at_field-based freshness thresholds to detect upstream delays before running dependent models.
• StarRocks-friendly singular tests: Write custom SQL assertions (e.g., null rate, volume anomalies, referential integrity, no future dates) and scope them to partitions for performance.
• Production-grade debugging: Enable store_failures so you can inspect failed rows after a run.
• Operational performance safeguards: Use pre-run ANALYZE to refresh stats and avoid slow/incorrect cost-based plans.

Quick Start

Tell your agent: "Generate dbt StarRocks tests for my models and sources, including freshness checks, partition-scoped singular SQL tests, and dbt-expectations integration, then output the schema.yml and tests/*.sql content ready to paste."