Introduction
Great Expectations is an open-source data validation framework that helps teams define, test, and monitor data quality through declarative expectations and automated checks. By enabling data testing as code, Great Expectations brings software engineering rigor to data workflows—ensuring that bad data is caught early, documented clearly, and prevented from propagating downstream. Expectations can play a key role in data observability and platform resilience, integrating with batch and streaming pipelines, warehouse layers, and orchestration tools.
Key benefits of using Great Expectations include:
Declarative Data Quality Rules: Allows teams to define “expectations” (e.g., column uniqueness, nullability, value ranges) using simple, human-readable syntax.
Automated Data Validation: Integrates seamlessly with tools like Airflow, dbt, and Spark to validate data at key pipeline checkpoints, staging layers, or warehouse syncs.
Rich Data Docs and Transparency: Generates living documentation from validation results, enabling collaboration between data producers, engineers, and analysts.
Flexible Integration: Supports multiple backends (e.g., Pandas, SQL, Spark) and connects to warehouses like Snowflake, BigQuery, and Delta Lake.
Early Warning System: Surfaces data quality issues proactively—reducing the risk of silent data drift, broken reports, and unreliable ML inputs.
Great Expectations is used to test incoming data from external sources, enforce schema integrity across dbt transformations, and validate outputs before data is consumed by dashboards or machine learning systems. It complements tools like DataHub (for metadata), Superset (for visualization), and MLflow (for model tracking), ensuring that the entire data stack operates on trusted, high-quality data.
Great Expectations creates a culture of data quality by default—empowering teams to deliver insights, features, and models built on a strong, verifiable foundation.