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
47,133 repositories indexed
1–12 of 12 · Most stars
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
- pandas-profiling
- pandas-dataframe
- statistics
- jupyter-notebook
Always know what to expect from your data.
- pipeline-tests
- dataquality
- datacleaning
- datacleaner
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
A light-weight, flexible, and expressive statistical data testing library
- pandas
- validation
- schema
- dataframes
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
Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.
- dataprep
- data-science
- datapreparation
- dataconnector
pyspark methods to enhance developer productivity 📣 👯 🎉
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
Canonical GTFS Validator project for schedule (static) files.
- gtfs-dataset
- gtfs-validator
- gtfs-zip
- gtfs-feed
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