1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.

Always know what to expect from your data.

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

Refine high-quality datasets and visual AI models

The Open Source Feature Store for Machine Learning

Compare tables within or across databases

⚡ Data quality testing for the modern data stack (SQL, Spark, and Pandas) https://www.soda.io

Automatically find issues in image datasets and practice data-centric computer vision.

Engine for ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.

Code review for data in dbt

The toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling.

FeatHub - A stream-batch unified feature store for real-time machine learning

The Lakehouse Engine is a configuration driven Spark framework, written in Python, serving as a scalable and distributed engine for several lakehouse algorithms, data flows and utilities for Data Products.

Offical Repo for "Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale"

数据治理、数据质量检核/监控平台（Django+jQuery+MySQL）

Possibly the fastest DataFrame-agnostic quality check library in town.

Great Expectations Airflow operator

pyDVL is a library of stable implementations of algorithms for data valuation and influence function computation

re_data - fix data issues before your users & CEO would discover them 😊

Installer for DataKitchen's Open Source Data Observability Products. Data breaks. Servers break. Your toolchain breaks. Ensure your team is the first to know and the first to solve with visibility across and down your data estate. Save time with simple, fast data quality test generation and execution. Trust your data, tools, and systems end to end.