Great Expectations

About Great Expectations

Comprehensive tool helping data teams validate, document, and profile their data. Define expectations for your data ensuring it meets quality standards before processing.

Key Features

• 1Expectation suites define data quality rules in Python or JSON
• 2Automatic data documentation ('Data Docs') generated from validation results
• 3Checkpoint system integrates validation into Airflow, Prefect, or CI
• 4Profiler tool auto-generates expectations from existing data distributions
• 5Supports pandas, Spark, SQLAlchemy, and cloud data warehouse backends

How Python Data Engineers Use Great Expectations

Data engineers integrate Great Expectations into pipelines as a quality gate — defining expectations for each dataset (row counts, column nullability, value ranges), then running a Checkpoint after each ingestion job to validate the data. Failed validations trigger alerts or halt the pipeline before bad data reaches the warehouse.