#Data-quality-checks
Showing 13 of 13 repositories tagged #data-quality-checks, ranked by stars
Data Contracts engine for the modern data stack. https://www.soda.io
re_data - fix data issues before your users & CEO would discover them π
Know your data betterοΌDatavines is Next-gen Data Observability Platform, support metadata manage and data quality.
Engine for AI/ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.
Possibly the fastest DataFrame-agnostic quality check library in town.
Code for blog at https://www.startdataengineering.com/post/python-for-de/
Swiple enables you to easily observe, understand, validate and improve the quality of your data
A production-ready PySpark project template with medallion architecture, Python packaging, unit tests, integration tests, CI/CD automation, Databricks Asset Bundles, and DQX data quality framework.
ETL / ELT / Reverse ETL Framework powered by DuckDB, designed to seamlessly integrate and process data from diverse sources. It leverages Markdown as a configuration medium, where YAML blocks define metadata for each data source, and embedded SQL blocks specify the extraction, transformation, and loading logic.
Data Quality Monitor (DQM) - Continuously validate your data with easy, customizable rules.
Safety net for machine learning pipelines. Plays nice with sklearn and pandas.
:zap: Prevent downstream data quality issues by integrating the Soda Library into your CI/CD pipeline.
πYour Data Quality Detector / Gain insight into your data and get it ready for use before you start working with it π‘ππ π