data-quality-frameworks

Enforce data quality with Great Expectations, dbt tests, and data contracts across pipelines.

3|1|Updated Feb 3, 2026

Search Tags:#dbt #data-pipelines #data-quality #great-expectations #data-validation #data-contracts

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npx skills add https://github.com/duanbiao2000/obsidianDoc26 --skill data-quality-frameworks-duanbiao2000

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-quality-frameworks
Source: https://github.com/duanbiao2000/obsidianDoc26/tree/main/agents-main/plugins/data-engineering/skills/data-quality-frameworks
Command: npx skills add https://github.com/duanbiao2000/obsidianDoc26 --skill data-quality-frameworks-duanbiao2000

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data quality is critical for trustworthy analytics; this framework provides production patterns for validating data with Great Expectations, dbt tests, and data contracts to ensure reliable pipelines.

Core Features & Use Cases

Integrates Great Expectations validation, dbt tests, and data contracts to enforce data quality across pipelines.
Provides patterns for unit, integration, and contract-based validations to catch data issues early.
Use cases include validating source data, validating ETL outputs, monitoring quality metrics, and automating CI/CD validations.

Quick Start

Install and configure Great Expectations, dbt tests, and data contracts to start validating data quality in your environment.

Frequently Asked Questions about data-quality-frameworks

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

FAQPage Schema

How do I integrate Great Expectations and dbt tests to enforce data quality in pipelines?▼

To enforce data quality, you integrate Great Expectations validation, dbt tests, and data contracts to catch data issues early across production and CI/CD workflows. This framework provides patterns for validating source data and ETL outputs.

What are data contracts and when do I need them for data validation?▼

Data contracts are deterministic checks used for schema validation to ensure reliable pipelines. You need them when validating datasets and monitoring quality metrics to enforce agreements between data producers and consumers.

Can I automate dbt tests and Great Expectations validation in CI/CD workflows?▼

Yes, you can automate data validation orchestrations across CI/CD workflows. The framework supports automating validations to monitor quality metrics and catch data issues early in production environments.

What is the best way to validate ETL outputs and monitor data quality metrics?▼

The best way to validate ETL outputs is by applying unit, integration, and contract-based validations. This approach monitors quality metrics across pipelines using Great Expectations and dbt tests.

Do I need dbt to use Great Expectations for schema validation and data contracts?▼

You do not need dbt exclusively, as Great Expectations handles deterministic checks independently. However, integrating dbt tests with data contracts provides comprehensive schema validation and automated validation orchestrations.

Why use data contracts instead of just dbt tests for pipeline data quality?▼

Data contracts provide schema validation and enforce deterministic checks beyond standard dbt tests. Combining them with Great Expectations ensures comprehensive data quality across pipelines, validating both source data and ETL outputs.