dQrev

dQrev is an open-source framework designed to support data quality reviews in data workflows. It provides tooling to define, execute, and document data quality rules, track data issues, and generate audit-ready reports suitable for governance processes.

The project originated from a community of data scientists and data governance professionals seeking greater transparency and reproducibility in data quality assessments. It is intended to integrate with common data platforms and programming languages, notably SQL and Python, to fit into existing data pipelines and tooling ecosystems.

Core components of dQrev include a rule engine that evaluates checks against datasets, a rule library containing predefined data quality checks, a data source adapter layer for connecting to various databases and file formats, and a reporting module that can produce dashboards and exportable artifacts for audits or reviews.

Typical usage involves ingesting datasets, configuring or selecting applicable checks, executing validations, reviewing results, assigning remediation actions, and archiving the assessment record for future reference and compliance purposes.

Distribution and adoption of dQrev occur through open-source channels, with community contributions that document usage patterns, integration guides, and example workflows. It is used in contexts such as data onboarding, regulatory compliance, risk assessment, and ongoing data governance programs, where traceability and reproducibility of quality assessments are required.

See also: data quality, data governance, reproducible research, data stewardship.