great-expectations-validator

# Great Expectations Validator Validate data quality using Great Expectations for comprehensive data testing, documentation, and quality monitoring. ## Overview This skill provides capabilities for data quality validation using Great Expectations (GX), the leading open-source library for data quality. It enables creation and execution of expectation suites, data documentation generation, and integration with ML pipelines. ## Capabilities ### Expectation Suite Management - Create and configure expectation suites - Define expectations for columns and tables - Validate data against expectations - Store and version expectation suites ### Data Validation - Schema validation (column presence, types) - Statistical validation (distributions, ranges) - Referential integrity checks - Custom SQL-based expectations - Regex pattern matching ### Data Documentation - Generate data documentation (Data Docs) - Create profiling reports - Document validation results - Build data dictionaries ### Pipeline Integration - Checkpoint configuration and execution - Batch request management - Action-based workflows (notifications, storage) - Integration with Airflow, Prefect, Dagster ### Custom Expectations - Define domain-specific expectations - Parameterized expectations - Multi-column expectations - Row-condition based expectations ## Prerequisites ### Installation ```bash pip install great_expectations>=0.18.0 ``` ### Optional Connectors ```bash # Database connectors pip install great_e...