Great Expectations
What it is: Python library for data validation and documentation. Write expectations about your data (tests), run them automatically, generate documentation. Like unit tests for data quality.
What It Does Best
Data testing framework. Assert that columns exist, values fall in ranges, no nulls where expected. Catch data quality issues before they break pipelines. Fail fast with clear error messages.
Automatic documentation. Expectations become living documentation. Generate beautiful HTML reports showing what your data should look like. Non-technical stakeholders understand data contracts.
Pipeline integration. Runs in Airflow, Prefect, Dagster, dbt. Validates data at every pipeline step. Prevents bad data from propagating downstream.
Key Features
Expectation library: 300+ built-in data validation tests
Data docs: Auto-generated HTML documentation of data quality
Profiling: Automatically generate expectations from sample data
Validation results: Detailed reports on what passed and failed
Integrations: Works with pandas, Spark, SQL databases, cloud storage
Pricing
Open source: Free, Apache 2.0 license (library)
Great Expectations Cloud: Paid SaaS for collaboration and monitoring
Enterprise: Custom pricing for support and advanced features
When to Use It
✅ Production data pipelines need quality checks
✅ Multiple people contributing data to shared systems
✅ Need to document data contracts
✅ Data quality issues cause downstream problems
✅ Want automated alerts when data doesn't match expectations
When NOT to Use It
❌ One-off data analysis (setup overhead)
❌ Very simple validation (pandas assertions easier)
❌ Team resistant to testing culture
❌ No production pipelines to monitor
❌ Learning curve too steep for current needs
Common Use Cases
ETL validation: Ensure transformed data meets quality standards
ML pipeline checks: Validate training data before model fitting
API data validation: Test data from external sources
Database migration: Verify data integrity after moving systems
Data SLAs: Monitor and report on data quality agreements
Great Expectations vs Alternatives
vs dbt tests: GE more comprehensive, dbt better for SQL workflows
vs Pandera: GE more enterprise features, Pandera simpler for pandas
vs Manual asserts: GE provides documentation and reporting
Unique Strengths
Expectation library: Hundreds of pre-built validation patterns
Data docs: Best-in-class automated documentation generation
Profiling: Learn expectations from existing data automatically
Enterprise adoption: Used by Fortune 500 companies in production
Bottom line: The standard for data quality testing in Python. Steep learning curve but worth it for production pipelines. Treat your data like code—test it. Prevents data disasters before they happen. Start small, one expectation at a time.