Great Expectations — Data Validation for AI Pipelines

Introduction

Great Expectations is the leading data validation framework, with 11,400+ GitHub stars. Write data tests the way you write unit tests — catch data quality issues before they break your AI/ML pipelines. Supports 300+ built-in expectations, auto-profiling, and auto-generated data docs. Ideal for data engineers and ML practitioners building production data pipelines. Supports Pandas, Spark, SQL databases, and more.

Great Expectations — Test Data Like You Test Code

Core Features

• 300+ built-in expectations — null checks, range validation, regex matching, statistical tests
• Auto-profiling — generate expectations from sample data
• Data docs — auto-generated HTML data quality reports
• Multi-backend — Pandas, Spark, PostgreSQL, BigQuery, and more
• Pipeline integration — Airflow, Dagster, Prefect, dbt

FAQ

Q: What is Great Expectations? A: An open-source data validation framework that lets you write data assertions like unit tests to catch issues before they impact AI/ML pipelines.

Q: Is it free? A: The open-source core is free (Apache-2.0); a paid cloud version is also available.