great_expectations open source analysis

Always know what to expect from your data.

Project overview

⭐ 11052 · Python · Last activity on GitHub: 2026-01-05

GitHub: https://github.com/great-expectations/great_expectations

Why it matters for engineering teams

Great Expectations addresses a common challenge in data engineering and machine learning workflows: ensuring data quality and consistency throughout the pipeline. It provides a practical framework for defining, testing, and documenting data expectations, which helps teams catch issues early and maintain trust in their data. This open source tool for engineering teams is particularly suited to machine learning and AI engineering roles that require robust data validation as part of their production pipelines. The project is mature and reliable enough for production use, with a strong community and extensive documentation. However, it may not be the right choice for teams looking for a lightweight or fully managed cloud solution, as it requires some setup and maintenance, especially when opting for the self hosted option for data validation.

When to use this project

Great Expectations is a strong choice when your team needs a production ready solution for automated data quality checks integrated into complex data pipelines. Consider alternatives if your requirements are limited to simple data profiling or if you prefer a fully managed cloud service without the overhead of self hosting.

Team fit and typical use cases

Machine learning engineers and AI teams benefit most from Great Expectations by embedding data quality tests within their model training and deployment workflows. Data engineers also use it to enforce data contracts and prevent pipeline debt in production systems. It commonly appears in products involving data science, MLOps, and exploratory data analysis where maintaining high data quality is critical.

Best suited for

Topics and ecosystem

cleandata data-engineering data-profilers data-profiling data-quality data-science data-unit-tests datacleaner datacleaning dataquality dataunittest eda exploratory-analysis exploratory-data-analysis exploratorydataanalysis mlops pipeline pipeline-debt pipeline-testing pipeline-tests

Activity and freshness

Latest commit on GitHub: 2026-01-05. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.