troubleshooting-dbt-job-errors

To diagnose a dbt job failure, analyze run logs and run_results.json to classify the error as infrastructure, code/compilation, or data/test related. This systematic review guides you through checking warehouse status and git history to pinpoint the root cause.

Overnight dbt Cloud job failures often stem from schema mismatches or source data changes. This Skill reviews run logs via the Admin API and git history to identify whether the failure is infrastructure, code, or data-related to resolve intermittent issues.

The best way to troubleshoot dbt data pipeline errors is to classify the failure type first. By querying the Admin API for error details and checking git history, you can isolate whether the issue originates from data anomalies or compilation errors.

You do not need the dbt Admin API if you can manually provide run artifacts. While the Skill can query the Admin API using tools like list_jobs_runs, it also fully supports diagnosing issues from user-provided logs and run_results.json files.

Yes, you can use dbt logs to fix SQL compilation errors by reviewing the error classification and checking git history. This Skill analyzes run_results.json and logs to identify code issues, guiding you through fixing models and creating pull requests.

To resolve dbt test failures caused by data issues, investigate source data changes using the discovering-data skill. This Skill identifies data-related failures from run logs and guides the investigation into schema mismatches in downstream models.