1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
- pandas-profiling
- pandas-dataframe
- statistics
- jupyter-notebook
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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
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
Pandas, Polars, Spark, and Snowpark DataFrame comparison for humans and more!
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
Lightweight DataFrame validation decorators for Pandas, Polars, Modin, and PyArrow. No custom types required.
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
Open, audit-grade agentic data quality framework with portable industry packs