Great Expectations

Great Expectations (GX) is the world's most popular open-source data quality framework, designed to help data teams define, test, and monitor the quality of their data throughout the entire data pipeline lifecycle. At its core, GX Core provides a Python-native engine that enables data engineers and analysts to create executable expectations (assertions about data properties) using a simple, declarative API that integrates seamlessly with familiar tools like Python, Pandas, Spark, and Jupyter notebooks. These expectations can validate column types, value ranges, uniqueness constraints, distribution properties, and complex statistical characteristics, providing a shared vocabulary that bridges the gap between technical and business stakeholders. The framework supports over 300 built-in expectation types covering common data quality checks, and allows users to create custom expectations for domain-specific validation rules. Data quality results are automatically rendered as interactive Data Docs, providing human-readable HTML reports that make it easy for all stakeholders to understand data quality status without reading code. GX integrates with major data sources including PostgreSQL, MySQL, Snowflake, BigQuery, Databricks, Spark, Pandas, and SQLAlchemy-connected databases. The platform offers Data Context for managing expectation suites across projects, Checkpoints for orchestrating validation runs, and integration with orchestrators like Airflow, Dagster, Prefect, and dbt. Trusted by data teams at thousands of organizations, GX helps prevent bad data from reaching production dashboards, ML models, and downstream consumers, reducing the costly consequences of data quality incidents.