Repository Index

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

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

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

A detective for your data. Zero-config data quality monitoring — works with dbt, Postgres, BigQuery, Snowflake. No YAML.

• anomaly-detection
• bigquery
• cli
• data-engineering