dbt Artifacts Package

What problem does it solve?

Gaining visibility into dbt project execution, tracking historical performance, and monitoring data quality across different dbt environments can be challenging without a centralized logging mechanism. This skill provides a robust solution for capturing and analyzing dbt execution metadata.

Core Features & Use Cases

• Cross-Platform Monitoring: Capture detailed execution logs for models, tests, and snapshots from any dbt environment (CLI, Cloud, Airflow).
• Historical Performance Analysis: Track model runtimes, test failures, and data quality metrics over time to identify regressions and optimize performance.
• Data Quality Trends: Monitor test pass/fail rates and identify flaky tests or data quality degradation patterns.
• Use Case: Implement dbt Artifacts to centralize all dbt run metadata. Then, build a BI dashboard to visualize daily test failures, identify the slowest-running models, and track overall data quality trends across your entire data platform.

Quick Start

Add to packages.yml

packages:

• package: brooklyn-data/dbt_artifacts version: 2.9.3

Add to dbt_project.yml

on-run-end:

• "{{ dbt_artifacts.upload_results(results) }}"

models: dbt_artifacts: +database: your_database +schema: dbt_artifacts

Install and initialize

dbt deps dbt run --select dbt_artifacts