Great Expectations (GX) is an open-source Python library for data validation, documentation, and profiling. It enables data teams to define “expectations” – declarative assertions about what data should look like – and automatically validate data against these expectations at any point in a pipeline. This approach treats data quality as a first-class engineering concern, catching data issues before they propagate to downstream models, dashboards, or business decisions.

The library’s core concept is the Expectation, a verifiable assertion about data such as “this column should never be null,” “values should be between 0 and 100,” or “this table should have between 1 million and 2 million rows.” Great Expectations ships with over 300 built-in expectation types covering nullity, uniqueness, value ranges, regular expressions, distributions, referential integrity, and more. Expectations are organized into Expectation Suites that can be versioned, shared, and applied to different data sources. The library connects to pandas DataFrames, Spark DataFrames, and SQL databases (PostgreSQL, MySQL, BigQuery, Snowflake, Redshift, and others) through its Datasource abstraction.

When validations run, Great Expectations generates Data Docs – automatically built, human-readable HTML documentation that shows the validation results, data statistics, and expectation definitions. This makes data quality visible and auditable across the organization. Great Expectations integrates with orchestration tools like Airflow, Prefect, and Dagster through checkpoint-based workflows. GX Cloud, a managed service, provides a collaborative UI for creating and managing expectations without code.

Key Capabilities

  • 300+ Built-In Expectations - Declarative assertions covering data types, ranges, distributions, uniqueness, referential integrity, and custom logic
  • Data Docs - Automatically generated HTML documentation of validation results, data profiles, and expectation definitions
  • Multi-Backend Support - Validate data in pandas, Spark, and SQL databases (PostgreSQL, BigQuery, Snowflake, Redshift, and more)
  • Pipeline Integration - Checkpoint system for embedding validations in Airflow, Prefect, Dagster, and other orchestration tools

Cloud Equivalents

Great Expectations is the open-source alternative to AWS Glue Data Quality, Azure Purview data quality, and Google Cloud Dataplex data quality rules. Cloud data quality services integrate natively with their respective data platforms, while Great Expectations provides a portable, code-first approach that works across any data infrastructure.

Origins and History

Great Expectations was created by Abe Gong and James Campbell and first released in 2018. The project is licensed under the Apache License 2.0. Superconductive (now GX), the company behind Great Expectations, was founded to provide commercial support and the GX Cloud managed service. The project has over 9,000 GitHub stars and is one of the most widely adopted open-source data quality tools. Great Expectations 1.0 was released in 2024 with a simplified API and improved performance.

Sources

  1. https://greatexpectations.io/
  2. https://github.com/great-expectations/great_expectations