Can I integrate this into a CI/CD pipeline?

Yes, the skill is designed to automate validation checks within deployment workflows to prevent low-quality data or schema-breaking changes from reaching production.

Can I use this for real-time data monitoring?

Yes, the patterns include checkpoint configurations and notification actions that can be integrated into your orchestration layer for real-time alerting on data quality failures.

Does it include dbt-specific tests?

Absolutely, it features patterns for generic dbt tests, singular business logic tests, and advanced validation using the dbt_utils package.

Which frameworks are supported by this skill?

This skill supports Great Expectations, dbt (Data Build Tool), SodaCL for data contracts, and general SQL-based validation patterns.

How does this help with Data Contracts?

It provides standardized YAML templates for defining schemas, quality specifications, and SLAs, facilitating clear agreements between data producers and consumers.

Data Quality Frameworks

Name: Data Quality Frameworks
Author: drgaciw

bydrgaciw

Database Management

Implements robust data validation and quality assurance using Great Expectations, dbt tests, and formal data contracts.

About

This skill provides production-ready patterns for maintaining high-integrity data pipelines by integrating industry-standard frameworks like Great Expectations and dbt. It enables developers to establish comprehensive data quality dimensions—such as completeness, uniqueness, and timeliness—while automating validation within CI/CD workflows. By implementing the data testing pyramid and formal data contracts, this skill ensures reliable data flow between producers and consumers, significantly reducing silent failures and maintaining trust in analytical and machine learning environments.

Key Features

0 GitHub stars
Multi-dimensional quality checks covering completeness, validity, and statistical drift
Comprehensive dbt test suites including generic, singular, and custom validation patterns
Implementation of formal Data Contracts for producer-consumer alignment
Automated Great Expectations suite generation and checkpoint orchestration
Integrated notification patterns for real-time data quality failure alerts via Slack

Use Cases

Implementing a structured testing pyramid for data to catch schema errors and business logic issues early
Building automated validation pipelines for cloud data warehouses like Snowflake or BigQuery
Establishing formal data contracts to prevent breaking changes in upstream data sources

About

Key Features

0 GitHub stars
Multi-dimensional quality checks covering completeness, validity, and statistical drift
Comprehensive dbt test suites including generic, singular, and custom validation patterns
Implementation of formal Data Contracts for producer-consumer alignment
Automated Great Expectations suite generation and checkpoint orchestration
Integrated notification patterns for real-time data quality failure alerts via Slack

Use Cases

Implementing a structured testing pyramid for data to catch schema errors and business logic issues early
Building automated validation pipelines for cloud data warehouses like Snowflake or BigQuery
Establishing formal data contracts to prevent breaking changes in upstream data sources