Great Expectations
Great Expectations is an open-source data quality framework that helps teams validate, document, and profile data. It enables automated testing of data pipelines and ensures data meets defined expectations before it’s used in analytics or machine learning workflows.
Key Features
Data Validation: Define and run “expectations” to catch anomalies and enforce data quality rules.
Data Docs: Auto-generates human-readable documentation for validation results.
Integration Friendly: Works with Pandas, Spark, SQL, Airflow, dbt, and major cloud platforms.
Custom Expectations: Create domain-specific checks and reusable logic.
CI/CD Support: Integrates into pipelines for continuous data quality checks.
Example Use Cases
Validating incoming data in ETL and ML workflows
Preventing pipeline failures due to schema drift or missing values
Ensuring compliance with data governance standards
Generating readable QA reports for data stakeholders