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data-quality-frameworks

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

Updated Mar 18, 2026
Search Tags:#data quality#data validation#dbt#data contracts#great_expectations#pipeline quality
One-click install
npx skills add https://github.com/ekremmkasap/jarvis --skill data-quality-frameworks-ekremmkasap
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-quality-frameworks
Source: https://github.com/ekremmkasap/jarvis/tree/main/server/agent_prompts/wshobson/plugins/data-engineering/skills/data-quality-frameworks
Command: npx skills add https://github.com/ekremmkasap/jarvis --skill data-quality-frameworks-ekremmkasap

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data teams need to enforce data quality across pipelines, ensuring reliable results and governance by combining Great Expectations, dbt tests, and data contracts.

Core Features & Use Cases

  • Implement comprehensive data quality checks with Great Expectations
  • Build and validate dbt tests across tables and models
  • Define and enforce data contracts to govern schemas and lineage
  • Continuous monitoring and CI/CD integration for automated quality validation

Quick Start

Configure Great Expectations, create an expectation suite, and run a daily checkpoint to validate data.

Frequently Asked Questions about data-quality-frameworks

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

FAQPage Schema
How do I validate data quality across batch and streaming pipelines?▼

Data contracts govern schemas and lineage by defining strict rules for data structure and quality. Enforcing these contracts ensures reliable pipeline results and supports contract-based data governance across your architecture.

What is the best way to integrate data validation with CI/CD?▼

The best way to integrate data validation with CI/CD is by configuring Great Expectations checkpoints and dbt tests to run automatically. This provides continuous monitoring and automated quality validation across your pipelines.

Can I perform cross-table validations using dbt tests and Great Expectations?▼

Yes, you can perform cross-table validations by building expectation suites and dbt tests across tables and models. This allows you to document quality rules and detect anomalies across related datasets.

Why do I need data contracts for pipeline quality governance?▼

Data contracts govern schemas and lineage by defining strict rules for data structure and quality. Enforcing these contracts ensures reliable pipeline results and supports contract-based data governance across your architecture.

Does this data quality approach work for both batch and streaming use cases?▼

Yes, this data quality approach supports both batch and streaming use cases. You can build expectation suites and checkpoints to continuously validate data quality and detect anomalies regardless of your pipeline architecture.

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