data-quality-frameworks

Implement data quality validation with Great Expectations, dbt tests, and data contracts.

Updated Mar 11, 2026

Search Tags:#validation #data quality #dbt #pipeline #data contracts #great expectations

Ready to run

npx skills add https://github.com/Industrial/rust-symphony --skill data-quality-frameworks-industrial

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-quality-frameworks
Source: https://github.com/Industrial/rust-symphony/tree/main/.cursor/skills/data-quality-frameworks
Command: npx skills add https://github.com/Industrial/rust-symphony --skill data-quality-frameworks-industrial

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the critical need for reliable data by implementing comprehensive data quality validation strategies, preventing data errors from propagating through pipelines.

Core Features & Use Cases

Automated Validation: Integrates Great Expectations, dbt tests, and data contracts for end-to-end data quality checks.
Pipeline Integration: Automates validation within CI/CD pipelines and scheduled jobs.
Use Case: When building a new data pipeline for customer analytics, use this Skill to set up Great Expectations for schema and value validation, dbt tests for referential integrity, and a data contract to define expected data characteristics with downstream consumers.

Quick Start

Implement data quality validation for your data pipelines using Great Expectations, dbt tests, and data contracts.

Frequently Asked Questions about data-quality-frameworks

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

FAQPage Schema

How do I set up data quality validation in my data pipeline?▼

Set up data quality validation by implementing Great Expectations for schema checks, dbt tests for referential integrity, and data contracts to define expected data characteristics for downstream consumers.

What is the best way to automate dbt tests and data contracts in CI/CD?▼

Automate dbt tests and data contracts in CI/CD by integrating validation rules into your scheduled jobs, enforcing data integrity across transformation stages automatically before pipeline execution.

When do I need data contracts in my data pipelines?▼

You need data contracts when establishing expected data characteristics with downstream consumers, preventing data errors from propagating through pipelines by defining strict validation rules.

Can I use Great Expectations and dbt tests together for data validation?▼

Yes, you can use Great Expectations and dbt tests together to implement end-to-end data quality checks, combining schema and value validation with referential integrity across various data sources.

Does data quality validation work across different data sources and transformation stages?▼

Data quality validation enforces data integrity across various data sources and transformation stages by applying validation rules and automated checks throughout the entire data pipeline workflow.

Why does my data pipeline need automated validation checks?▼

Your data pipeline needs automated validation checks to prevent data errors from propagating downstream, ensuring reliable data through comprehensive validation strategies using Great Expectations and dbt tests.