data-quality-framework

Know your data better！Datavines is Next-gen Data Observability Platform, support metadata manage and data quality.

Swiple enables you to easily observe, understand, validate and improve the quality of your data

Data quality, maturity and utility labelling tool for the EHDS (HealthData@EU)

Sumeh — Unified Data Quality Framework Sumeh is a unified data quality validation framework supporting multiple backends (PySpark, Dask, Polars, DuckDB, Pandas) with centralized rule configuration.

Automatically validate datasets, poll task status, and display validation results in a GitHub using Swiple pull request.

A production-ready, config-driven Data Quality framework powered by databricks-labs-dqx. It converts plain-language business rules into scalable checks featuring Delta auditing, drift detection, SLA enforcement, bad-row quarantine, and end-to-end DAB deployment.

Gere relatórios automatizados de cotações de ETFs da B3. Extraia dados via API ou offline, valide qualidade, calcule métricas financeiras e gere relatórios PDF profissionais com Data Lake (ELT).

数据质量检查工具, 用于诊断数据的问题

Data Quality Library (dqLib): An R Package for Explainable and Traceable Assessments of Clinical Data Quality

Production-grade Customer 360 data architecture featuring canonical modeling, deterministic identity resolution, attribute survivorship, and DQ gates in Python.

Microsoft Fabric end-to-end sample: medallion lakehouse, PySpark cleansing with reject handling, Direct Lake semantic model, Data Factory pipeline

Audit a CSV before you trust it. A local data-audit CLI that characterizes columns, gates each check on the properties it needs, and reports what it couldn't check.