great_expectations

Always know what to expect from your data.

11.1k

Stars

+231

Gained

2.1%

Growth

Python

Language

View on GitHub → ↑0.1% this week

💡 Why It Matters

Great Expectations addresses a critical challenge for engineering teams by ensuring data quality and integrity throughout the data pipeline. This open source tool is particularly beneficial for ML/AI teams, as it allows them to set expectations for data, automate validation, and catch issues early in the process. With over 11,000 stars and a steady growth of 231 stars in 96 days, it demonstrates a strong and engaged community, indicating its production-ready status and maturity. However, it may not be the right choice for teams needing a lightweight solution or those with extremely simple data validation needs.

🎯 When to Use

Great Expectations is a strong choice when teams require a robust framework for data profiling and quality assurance in complex ML/AI projects. Teams should consider alternatives if they are looking for a simpler, less resource-intensive solution.

👥 Team Fit & Use Cases

Data engineers, data scientists, and ML engineers are the primary users of Great Expectations. It is commonly integrated into data pipelines, machine learning workflows, and analytics platforms to ensure data quality and reliability.

🎭 Best For

Machine Learning and AI Engineer

🏷️ Topics & 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

Latest commit: 2026-02-14. Over the past 97 days, this repository gained 231 stars (+2.1% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.