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

Data Quality and Observability platform for the whole data lifecycle, from profiling new data sources to full automation with Data Observability. Configure data quality checks from the UI or in YAML files, let DQOps run the data quality checks daily to detect data quality issues.

A program designed to analyse a dataset and provide summary data regarding both categorical and continuous data in the form of a CSV file. The summary data includes means, medians, modes, cardinality and more. This data can be used as part of a data quality report to easily see potential issues/problems so they can be caught early and quickly.

Collection of R scripts to test packages in conducting data quality assessments

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

A function that automatically generates a Data Quality Report for your data

Data quality made simple

collection of Jupyter Notebooks in both English and Spanish, dedicated to performing data quality analysis using the R programming language

Data quality report made for machine learning.

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

A GitHub repo of Python scripts for data engineering, featuring pip wheel management, high-speed fuzzy matching (RapidFuzz), data profiling (pandera, y_dataquality), and seamless Oracle DB connectivity with cx_Oracle and SQLAlchemy. Ideal for building robust, efficient, and modern data workflows.