The Open Context Layer for Data and AI , OpenMetadata is the open platform for building trusted data context and business semantics for humans, AI assistants, and agents.

Data Contracts engine for the modern data stack. https://www.soda.io

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

Know your data better！Datavines 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.

🐳 Tool to automate data quality checks on data pipelines

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

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.

数据治理、数据质量检核/监控平台（Django+jQuery+MySQL）

An RDF Unit Testing Suite

Robotics Data Toolkit | Convert between robotics dataset formats (RLDS, LeRobot v2/v3, Zarr, HDF5, Rosbag). Inspect, visualize, and analyze datasets. Works with HuggingFace Hub. Built for OpenVLA, Octo, LeRobot, and Diffusion Policy workflows.

Code for blog at https://www.startdataengineering.com/post/python-for-de/

Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in production.

NBi is a testing framework (add-on to NUnit) for Business Intelligence and Data Access. The main goal of this framework is to let users create tests with a declarative approach based on an Xml syntax. By the means of NBi, you don't need to develop C# or Java code to specify your tests! Either, you don't need Visual Studio or Eclipse to compile y…

A Stata template for running high frequency checks of incoming research data at Innovations for Poverty Action

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

Lightweight library to write, orchestrate and test your SQL ETL. Writing ETL with data integrity in mind.

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.