Great Expectations is the leading open-source framework for data validation and testing with Python-first approach. Core library (Apache 2.0) is free. GX Cloud provides hosted service with collaboration features at $50/user/month (Teams) or custom enterprise pricing. The framework uses “Expectations”—declarative assertions about data (column values should be unique, not null, within range, etc.) that can be versioned, shared, and automatically generated from profiling. Data Docs feature generates human-readable documentation from test results. Over 300 built-in expectations covering nullness, uniqueness, value ranges, regex patterns, and statistical properties. Integrates with Airflow, Prefect, Dagster, dbt, and major orchestration tools. Supports pandas DataFrames, Spark DataFrames, and SQL databases. Best suited for data engineering teams wanting comprehensive validation with Python integration. Page should cover: expectations library overview, auto-profiling capabilities, Data Docs configuration, orchestrator integration, GX Cloud features vs open-source, comparison with dbt tests and Soda, and implementation best practices.