Can I use this for real-time data monitoring?

Yes, it includes patterns for automated quality pipelines and Great Expectations checkpoints that can be triggered immediately after data ingestion or transformation.

Which data quality tools does this skill support?

This skill provides production-ready patterns for Great Expectations, dbt (Data Build Tool) testing, and Data Contract specifications like SodaCL.

How does this help with Data Contracts?

It provides standardized YAML patterns to define schemas, quality specifications, and SLAs that serve as a formal, version-controlled agreement between data producers and consumers.

Does it include custom dbt tests?

Yes, it provides templates for generic dbt tests such as row count ranges, sequential value checks, and singular business rule assertions.

Data Quality Frameworks

Name: Data Quality Frameworks
Author: pur3v4d3r

bypur3v4d3r

•

Database Management

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

This skill provides standardized production patterns for establishing data integrity throughout the modern data stack. It enables developers to implement sophisticated validation suites using Great Expectations, build comprehensive dbt test layers, and define machine-readable data contracts between teams. By focusing on the six dimensions of data quality—completeness, uniqueness, validity, accuracy, consistency, and timeliness—this skill ensures reliable data pipelines and prevents downstream failures through automated CI/CD integration and real-time monitoring patterns.

Key Features

01Standardized Data Contract definitions using YAML and SodaCL

021 GitHub stars

03Great Expectations suite and checkpoint implementation

04Automated data quality pipeline orchestration

05Advanced dbt test patterns and custom generic tests

06Validation mapping across 6 key data quality dimensions

Use Cases

01Enforcing schema and business rules in production data warehouses

02Establishing formal data SLAs and contracts between engineering and business teams

03Preventing broken data from reaching downstream BI tools or ML models

Key Features

01Standardized Data Contract definitions using YAML and SodaCL

021 GitHub stars

03Great Expectations suite and checkpoint implementation

04Automated data quality pipeline orchestration

05Advanced dbt test patterns and custom generic tests

06Validation mapping across 6 key data quality dimensions

Use Cases

01Enforcing schema and business rules in production data warehouses

02Establishing formal data SLAs and contracts between engineering and business teams

03Preventing broken data from reaching downstream BI tools or ML models