---
id: imp-data-quality-frameworks
name: "data-quality-frameworks"
url: https://skills.yangsir.net/skill/imp-data-quality-frameworks
author: sickn33
domain: data-ai
tags: ["Data Quality", "Great Expectations", "Data Validation", "Data Governance"]
install_count: 3100
rating: 4.40 (124 reviews)
github: https://github.com/sickn33/antigravity-awesome-skills
---

# data-quality-frameworks

> 利用Great Expectations等框架实现数据质量验证，确保数据准确性和可靠性。

**Stats**: 3,100 installs · 4.4/5 (124 reviews)

## Before / After 对比

### 实施数据质量框架提升数据可信度

| Metric | Before | After | Change |
|---|---|---|---|
| - | - | - | - |
| - | - | - | - |
| - | - | - | - |

## Readme

# Data Quality Frameworks

Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines.

## Use this skill when

- Implementing data quality checks in pipelines
- Setting up Great Expectations validation
- Building comprehensive dbt test suites
- Establishing data contracts between teams
- Monitoring data quality metrics
- Automating data validation in CI/CD

## Do not use this skill when

- The data sources are undefined or unavailable
- You cannot modify validation rules or schemas
- The task is unrelated to data quality or contracts

## Instructions

- Identify critical datasets and quality dimensions.
- Define expectations/tests and contract rules.
- Automate validation in CI/CD and schedule checks.
- Set alerting, ownership, and remediation steps.
- If detailed patterns are required, open `resources/implementation-playbook.md`.

## Safety

- Avoid blocking critical pipelines without a fallback plan.
- Handle sensitive data securely in validation outputs.

## Resources

- `resources/implementation-playbook.md` for detailed frameworks, templates, and examples.


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*Source: https://skills.yangsir.net/skill/imp-data-quality-frameworks*
*Markdown mirror: https://skills.yangsir.net/api/skill/imp-data-quality-frameworks/markdown*