Great Expectations
Great Expectations is an open source data quality and validation framework that helps enterprises define, test, and monitor expectations about their data across pipelines and analytical environments.
- Open source framework for expressing data quality rules as machine-readable “expectations”
- Data validation and testing across ETL/ELT pipelines, analytics, and Machine Learning (ML) workflows (data quality)
- Integration with common data platforms, warehouses, and orchestration tools (data engineering)
- Human-readable documentation and data quality reports generated from expectations (data observability)
- Collaboration tooling around shared expectations, test suites, and data documentation for teams
More About Great Expectations
Great Expectations provides a declarative framework for data quality (data quality) that is used by engineering, analytics, and data science teams to formalize assumptions about data and verify those assumptions in automated workflows. The framework centers on “expectations,” which are machine-readable, test-like statements that describe properties of data, such as ranges, uniqueness, completeness, or schema constraints. These expectations can be executed against datasets to validate data at ingestion, during transformation steps, or prior to downstream consumption.
The platform supports integration into modern data stack components (data engineering), including data warehouses, data lakes, and distributed processing engines, by connecting to data through standardized interfaces such as SQLAlchemy for relational systems and native connectors for file-based or Pandas- and Spark-based data. This enables organizations to embed data validation directly into Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) pipelines, orchestration workflows, and Continuous Integration and Continuous Deployment (CI/CD) processes, so that data quality checks run as part of production data operations.
Great Expectations generates human-readable documentation and data quality reports (data observability) from expectations and validation results. These artifacts include data docs that summarize which expectations are defined for a given dataset and whether recent validation runs have passed or failed. This supports auditability and communication among technical and business stakeholders, giving visibility into data quality status and changes over time.
The framework is often used alongside orchestration and workflow tools such as Apache Airflow or other schedulers, where validation steps are added as tasks in broader pipelines. By version-controlling expectation suites and configuration files, teams can align data quality practices with software engineering methods, including code review, automated testing, and environment promotion.
From an architectural perspective, Great Expectations separates the definition of expectations, the configuration of “data sources” and “datasuites,” and the storage of validation results and documentation. This separation allows organizations to plug the framework into varied infrastructure, including cloud data platforms, on-premises (on-prem) databases, and hybrid environments. Configuration-driven deployment patterns support repeatable validation across development, staging, and production environments with minimal duplication.
Within an enterprise directory or marketplace taxonomy, Great Expectations aligns with data quality, data observability, and data engineering categories. It addresses use cases such as schema enforcement, data contract validation between producing and consuming teams, regression detection after pipeline changes, and compliance-related checks on data completeness or format. By encoding expectations as both executable tests and documentation, it supports collaboration between data engineers, analysts, and governance teams around a shared, versioned definition of data quality rules.