Great Expectations

Overview

Great Expectations is an open source Python library for data quality validation. It lets you define expectations about your data, run automated checks against datasets, and generate human readable documentation of data quality.

Best for

Best for
Data engineers and analysts who need a rigorous, open source way to validate data quality and documentation.

Use cases

• Validate incoming data pipelines against predefined quality rules
• Generate data documentation and quality reports automatically
• Monitor data drift in production by comparing expectations over time

Notes

Great Expectations is an open source Python library for data quality validation. It lets you define expectations about your data, run automated checks against datasets, and generate human readable documentation of data quality.

11,532 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.

Use cases

• Validate incoming data pipelines against predefined quality rules
• Generate data documentation and quality reports automatically
• Monitor data drift in production by comparing expectations over time

Pros

• Well documented with a large community (over 11,500 GitHub stars)
• Declarative API makes it easy to define and version control data expectations
• Integrates with common data tools like Pandas, Spark, and SQL databases

Cons

• Steep learning curve for users new to data quality concepts
• Performance can slow down on very large datasets without careful tuning
• Expectation definitions require consistent maintenance as data schemas evolve

Indexed from awesome-llmops and enriched against its public facts.