What tools are supported by the Data Quality Frameworks skill?

This skill provides implementation patterns for Great Expectations, dbt (Data Build Tool), SodaCL, and standardized YAML-based data contracts.

What are data contracts in the context of this skill?

Data contracts are formal agreements between data producers and consumers that define the schema, quality requirements, and SLAs to prevent breaking changes in downstream systems.

Can I integrate these quality checks into my CI/CD pipeline?

Yes, the skill includes patterns for automating Great Expectations and dbt tests within deployment workflows to catch data issues before they reach production.

How does this skill help with data reliability?

It provides production-ready templates for the Data Testing Pyramid, covering schema validation, uniqueness checks, business logic testing, and data freshness monitoring.

Data Quality Frameworks

Name: Data Quality Frameworks
Author: duanbiao2000

byduanbiao2000

0•

Database Management

Implements automated data validation and quality assurance workflows using Great Expectations, dbt tests, and data contracts.

This skill empowers data engineers to build reliable, production-grade pipelines by implementing the Data Testing Pyramid. It provides comprehensive patterns for defining data quality dimensions such as completeness, uniqueness, and timeliness. Users can quickly set up Great Expectations suites, configure dbt test suites for data warehouses, and establish formal data contracts to ensure consistency across teams. Whether you are automating validation in CI/CD or monitoring real-time data freshness, this skill provides the architectural blueprints and implementation patterns needed to maintain high-integrity data ecosystems.

Key Features

01Automated quality pipeline patterns for CI/CD and production monitoring

02Great Expectations suite and checkpoint configuration for Python-based validation

03Standardized data contract definitions for cross-team alignment and SLA management

04Comprehensive dbt data testing patterns including generic and singular tests

050 GitHub stars

06Multi-dimensional quality mapping for completeness, accuracy, and timeliness

Use Cases

01Validating incoming raw data batches before ETL processing using Great Expectations

02Defining and enforcing schemas between data producers and consumers via YAML-based data contracts

03Enforcing data integrity in cloud warehouses like Snowflake, BigQuery, or Redshift via dbt

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add duanbiao2000/obsidiandoc26 data-quality-frameworks

For use in Claude.ai and ChatGPT

Download Skill