provero-org/provero
Declarative data quality engine. Define checks in YAML, run anywhere.
Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
Appears on
Quick read
Latest capture 2026-09-07 03:07
Archive status reflects the last catalog sync, not a maintenance assessment. Check recent releases, issue responses, and fit for your project with the repository shortlisting checklist.
0 paths
Agent instructions and tool configuration found in this repository.
No config files detected.
7 observed captures since 2026-05-25. Observed captures are shown by default.
Stars from first capture +25
Observed captures only
All tracked data
Observed snapshots
Observed snapshots
Nearest indexed repositories by embedding similarity.
Declarative data quality engine. Define checks in YAML, run anywhere.
Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool
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.
Open, audit-grade agentic data quality framework with portable industry packs
Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.
Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learn...