[Deequ](https://github.com/awslabs/deequ) is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets. Python users may also be interested in PyDeequ, a Python interface for Deequ. You can find PyDeequ on [GitHub](https://github.com/awslabs/python-deequ), [readthedocs](https://pydeequ.readthedocs.io/en/latest/README.html), and [PyPI](https://pypi.org/project/pydeequ/). ## Deequ Official Documentation https://github.com/awslabs/deequ/tree/master ## Deequ Advantages - Built on [[Apache Spark]] for large datasets ## Deequ Disadvantages #placeholder/description %% wiki footer: Please don't edit anything below this line %% ## This note in GitHub <span class="git-footer">[Edit In GitHub](https://github.dev/data-engineering-community/data-engineering-wiki/blob/main/Tools/Data%20Quality/Deequ.md "git-hub-edit-note") | [Copy this note](https://raw.githubusercontent.com/data-engineering-community/data-engineering-wiki/main/Tools/Data%20Quality/Deequ.md "git-hub-copy-note")</span> <span class="git-footer">Was this page helpful? [👍](https://tally.so/r/mOaxjk?rating=Yes&url=https://dataengineering.wiki/Tools/Data%20Quality/Deequ) or [👎](https://tally.so/r/mOaxjk?rating=No&url=https://dataengineering.wiki/Tools/Data%20Quality/Deequ)</span>