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dbt Artifacts Package

Track dbt run history and execution metadata with the dbt_artifacts package.

34|13|Updated Feb 1, 2022
Search Tags:#performance#monitoring#analytics#data quality#ETL#observability#dbt
One-click install
npx skills add https://github.com/sfc-gh-dflippo/snowflake-dbt-demo --skill dbt-artifacts-package
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dbt Artifacts Package
Source: https://github.com/sfc-gh-dflippo/snowflake-dbt-demo/tree/main/.claude/skills/dbt-artifacts
Command: npx skills add https://github.com/sfc-gh-dflippo/snowflake-dbt-demo --skill dbt-artifacts-package

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires brooklyn-data/dbt_artifacts, and includes scripts (resource) and references (resource) components.

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

Frequently Asked Questions about dbt Artifacts Package

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I monitor dbt model performance and test failures over time?▼

Monitor dbt execution metadata using the dbt_artifacts package to track model runtimes, test pass/fail rates, and data quality trends. It captures detailed logs from any dbt environment—CLI, Cloud, or Airflow—and enables historical analysis to identify performance regressions and flaky tests.

Can I track dbt run metadata across different dbt versions and platforms?▼

Yes. The dbt_artifacts package works across any dbt version and platform to capture run history, execution metadata, test results, and model lineage. It standardizes logging via command_invocation_id and node_id, supporting per-run invocations, status, duration, rows affected, and compile time.

What data does dbt_artifacts capture from each dbt run?▼

dbt_artifacts captures run status, model and test execution duration, rows affected, compile time, run context (command, environment, dbt_version, threading), and model lineage dependencies. All data is stored with command_invocation_id and node_id for programmatic access and historical trend analysis.

How do I set up dbt_artifacts to start logging execution metadata?▼

Add brooklyn-data/dbt_artifacts to packages.yml, configure the on-run-end hook in dbt_project.yml, specify your target database and schema, then run dbt deps and dbt run --select dbt_artifacts. The package automatically captures metadata from subsequent dbt invocations.

Can I use dbt_artifacts to build a data quality dashboard?▼

Yes. After implementing dbt_artifacts, you can query the logged execution metadata to build dashboards showing daily test failures, slowest-running models, and overall data quality trends. The stored metadata enables BI visualization and programmatic alerting on data quality degradation patterns.

Do I need a specific dbt environment to use dbt_artifacts?▼

No. dbt_artifacts works with any dbt deployment—local CLI, dbt Cloud, or orchestration tools like Airflow. It centralizes metadata from all environments into a single schema, making it applicable across heterogeneous dbt setups.

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