GX (Great Expectations)
Declarative data quality testing for pipelines
GX (Great Expectations) is profiled here as a Data Quality tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
GX, formerly Great Expectations, is an open-source data quality framework created by Abe Gong and James Campbell. Teams declare Expectations, verifiable assertions such as column ranges, null thresholds, and schema rules, then validate dataframes and warehouse tables against them inside pipelines. GX Cloud adds a managed interface where analysts create and monitor validations without writing code. Validation results render as readable data docs, which turn quality checks into documentation stakeholders can review without code access.
Key Capabilities:
Library of prebuilt Expectations for common data quality rules
Validation across Pandas, Spark, Snowflake, Databricks, and SQL sources
Checkpoints that gate pipeline runs on validation results
Automatic data documentation from validation outcomes
Airflow and Dagster integrations for in-pipeline checks
Apache 2.0 core with the managed GX Cloud service
Alternative tools
- Datafold
Data diffing and regression testing for data teams
- Anomalo
Automated data quality monitoring with machine learning
- Elementary
dbt-native data observability and anomaly detection
- Soda
Data quality testing defined in a readable check language