Which data quality tools are supported?

This skill provides specific implementation patterns for Great Expectations, dbt (Data Build Tool) testing frameworks, and DataContract.com specifications.

Does this skill help with data freshness monitoring?

Absolutely. It includes specific patterns for checking timeliness and recency dimensions to ensure your data pipelines are delivering up-to-date information.

Can I automate data quality alerts?

Yes, the skill includes patterns for configuring checkpoints that can trigger actions like Slack notifications or pipeline failures based on validation results.

How do data contracts work with this skill?

The skill provides YAML-based templates for defining data contracts that specify schemas, quality rules (SodaCL), and SLAs for data assets.

Data Quality Frameworks

Name: Data Quality Frameworks
Author: wshobson

bywshobson

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23,194

Security & Testing

Implements robust data validation using Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines.

About

This skill provides production-ready patterns for establishing comprehensive data quality frameworks across the modern data stack. It enables developers to implement automated validation suites using Great Expectations, define structured dbt test patterns, and enforce formal data contracts between engineering teams. By focusing on critical data dimensions like completeness, uniqueness, and timeliness, this skill helps integrate data quality checks directly into CI/CD pipelines, preventing downstream data corruption and ensuring that analytics and machine learning models operate on high-fidelity data.

Key Features

Great Expectations suite and checkpoint configurations
Standardized data contract definitions for cross-team alignment
Automated data quality monitoring with Slack notification integration
23,194 GitHub stars
Validation patterns for data dimensions like accuracy and freshness
Advanced dbt testing patterns including generic and singular tests

Use Cases

Validating raw data ingestion pipelines before processing
Enforcing schema and quality contracts between data producers and consumers
Automating regression testing for data models in CI/CD environments

About

Key Features

Great Expectations suite and checkpoint configurations
Standardized data contract definitions for cross-team alignment
Automated data quality monitoring with Slack notification integration
23,194 GitHub stars
Validation patterns for data dimensions like accuracy and freshness
Advanced dbt testing patterns including generic and singular tests

Use Cases

Validating raw data ingestion pipelines before processing
Enforcing schema and quality contracts between data producers and consumers
Automating regression testing for data models in CI/CD environments