Repository Index

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

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

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

Build data pipelines with SQL and Python, ingest data from different sources, add quality checks, and build end-to-end flows.

• analytics
• bigquery
• data-modeling
• data-pipelines

Library for exploring and validating machine learning data

TranslatedOntiCards is an enterprise-oriented AI Data Hub that offers one-stop capabilities around business databases: data source connection, metadata understanding, natural language query, cross-source fusion, data quality inspection, query auditing, data asset management, and more

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 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

Data validator agains Splunk Common Information Model (CIM)