Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

Always know what to expect from your data.

Compare tables within or across databases

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

ML powered analytics engine for outlier detection and root cause analysis.

Library for Semi-Automated Data Science

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

Open Source Data Quality Monitoring.

Find data quality issues and clean your data in a single line of code with a Scikit-Learn compatible Transformer.

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.

DataOps Data Quality TestGen is part of DataKitchen's Open Source Data Observability. DataOps TestGen delivers simple, fast data quality test generation and execution by data profiling,  new dataset hygiene review, AI generation of data quality validation tests, ongoing testing of data refreshes, & continuous anomaly monitoring

Datailot-cli is the command line interface for accessing the AI teammate for engineers to ensure best practices in their SQL and dbt projects.

⚡ Prevent downstream data quality issues by integrating the Soda Library into your CI/CD pipeline.

Run greatexpectations.io on ANY SQL Engine using REST API. Supported by FastAPI, Pydantic and SQLAlchemy as best data quality tool

Code for data quality with greatexpectations blog

Make your dataset talk to you. The AI assistant for data preparation.

Open source clients for working with Data Culpa Validator services from data pipelines

Quality Aware Feature Store

A lightweight simple data quality testing tool.

data and pipeline testing with and for SQL