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Great Expectations helps teams ensure data quality and comply with regulatory requirements. It features a user-friendly API and various integrations that facilitate easy implementation into existing workflows.
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Great Expectations is an open-source framework that defines, validates, and documents data-quality rules as executable expectations.
The project began in 2017 to apply testing principles from software development to data pipelines and analytical data sets.
A data source connects storage, data assets, and batches. Expectation suites describe testable properties such as types, value ranges, or uniqueness; checkpoints execute them. Validation results and Data Docs record findings and can trigger pipeline actions.
Declarative rules state expected properties of a data set.
Checkpoints test concrete batches and produce structured results.
Data Docs make rules, runs, and deviations traceable.
Great Expectations supports data contracts, pipeline gates, and transparent quality control. Rules need business justification, maintenance, and drift calibration; many green checks still do not guarantee that data is fit for use.
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