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data-quality-frameworks

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

14|2|Updated Feb 24, 2026
Search Tags:#dbt#pipelines#quality-gates#data-quality#great-expectations#data-validation#data-contracts
One-click install
npx skills add https://github.com/andikarachman/data-science-plugin --skill data-quality-frameworks-andikarachman
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-quality-frameworks
Source: https://github.com/andikarachman/data-science-plugin/tree/main/skills/data-quality-frameworks
Command: npx skills add https://github.com/andikarachman/data-science-plugin --skill data-quality-frameworks-andikarachman

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Production-grade data quality validation patterns using Great Expectations, dbt tests, and data contracts to enforce repeatable quality gates across pipelines.

Core Features & Use Cases

  • Formal, reusable validation rules and contracts for data quality across teams.
  • Ready-to-adapt patterns for suites, tests, and checks that integrate with CI/CD.
  • Use Case: Standardize quality checks for warehousing ETL, streaming inputs, and data contracts between producers and consumers.

Quick Start

Run a sample data quality validation against a test dataset to generate a failure report.

Frequently Asked Questions about data-quality-frameworks

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

FAQPage Schema
How do I automate data quality validation in ETL pipelines?▼

Data contracts enforce schema and quality agreements between data producers and consumers by applying versioned validation rules and cross-tool consistency checks, ensuring pipeline inputs satisfy repeatable quality gates before downstream processing.

Can I integrate data quality checks with CI/CD workflows?▼

Great Expectations provides formal, reusable validation suites for data pipelines, while dbt tests focus on SQL-based logic within data warehouse transformations; combining both enforces cross-tool consistency and comprehensive contract validation.

Do I need data contracts to standardize quality checks for warehousing ETL?▼

Data contracts are needed to standardize quality checks for warehousing ETL because they define formal agreements between producers and consumers, enabling repeatable validation gates and versioned rule enforcement across streaming inputs and pipelines.

What are the limitations of using dbt tests for data quality validation?▼

Dbt tests are limited to SQL-based logic within data warehouse transformations and lack broader contract enforcement capabilities; cross-tool consistency requires supplementing them with Great Expectations for comprehensive pipeline validation.

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