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.
- metadata
- datadiscovery
- dataquality
- data-profiling
Repository Index
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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.
- weak-supervision
- data-cleaning
- data-quality
- noisy-labels
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
- data-drift
- jupyter-notebook
- pandas-dataframe
- machine-learning
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
- machine-learning
- ml
- model-validation
- data-validation
Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
- dataquality
- spark
- unit-testing
- scala
An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈
- ai-pipelines
- approximate-statistics
- statistical-properties
- data-quality
Data Contracts engine for the modern data stack. https://www.soda.io
- python
- data-engineering
- data-governance
- data-monitoring
The dbt-native data observability solution for data & analytics engineers. Monitor your data pipelines in minutes. Available as self-hosted or cloud service with premium features.
- data-lineage
- data-governance
- data-warehouse
- snowflake
Scalable data pre processing and curation toolkit for LLMs
- data-curation
- llm
- data
- data-prep
Utility functions for dbt projects.
Utility to compare data between homogeneous or heterogeneous environments to ensure source and target tables match
Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.
- data-analytics
- data-cleaning
- data-cleansing
- data-engineering
Data validation toolkit for assessing and monitoring data quality.
- data-validation
- tabular-data
- data-testing
- 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.
- data-ops
- data-quality
- data-quality-checks
- data-quality-measurement
Generate the smallest test dataset that provably catches broken data-mapping rules in ETL pipelines.
Field-level assurance for data pipelines: mapping-driven source-to-target validation, fault-injection test scoring and explainable risk-adaptive test prioritization.
Declarative data quality engine. Define checks in YAML, run anywhere.
- airflow
- anomaly-detection
- data-contracts
- data-quality
A detective for your data. Zero-config data quality monitoring — works with dbt, Postgres, BigQuery, Snowflake. No YAML.
- anomaly-detection
- bigquery
- cli
- data-engineering
Validates tabular CSV data using predefined validations, inspired from its Python homologue "Great Expectations".
- common-lisp
- csv
- data-validation
- great-expectations
Open, audit-grade agentic data quality framework with portable industry packs