Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
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Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
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Stars38,174
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LanguagePython
LicenseMIT
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</> SKILL.md
data-quality-frameworks.SKILL.md
---name: data-quality-frameworks
description: Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
---# Data Quality Frameworks
Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines.
## When to Use This Skill
- 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
## Core Concepts
### 1. Data Quality Dimensions
| Dimension | Description | Example Check |
| ---------------- | ------------------------ | -------------------------------------------------- |
| **Completeness** | No missing values | `expect_column_values_to_not_be_null` |
| **Uniqueness** | No duplicates | `expect_column_values_to_be_unique` |
| **Validity** | Values in expected range | `expect_column_values_to_be_in_set` |
| **Accuracy** | Data matches reality | Cross-reference validation |
