Data Validation with Great Expectations

Great Expectations, currently documented as GX Core, lets teams define Expectations—verifiable assertions about data—and organize them into Expectation Suites. Examples include checking that a column is not null, that values fall within an acceptable range, or that expected columns are present. A validation compares a batch of data against those rules and returns results. In the current GX Core workflow, data sources and assets identify where data come from; Batch Definitions select data; Validation Definitions connect a batch and suite; and Checkpoints run validations and can perform configured Actions. Actions may update Data Docs or send notifications. The current documentation differs from older Great Expectations tutorials, so code examples should match the installed version rather than assume pre-1.0 interfaces. GX detects whether configured assertions pass. It does not determine whether a threshold reflects valid policy, whether data are representative, or whether a failure should block a production pipeline. It also does not automatically clean or correct rows by default. Teams need to inspect results, investigate causes, and decide whether to stop, quarantine, or repair data. A passing suite only means the tested batch satisfied the rules actually defined. Data Docs can present expectations and validation results in human-readable form. They can aid review, but they are not proof that a dataset is correct or safe. Validation should complement schema checks, statistical monitoring, provenance, access controls, and domain review. Maintain versioned suites and review rules when schemas, products, or real-world distributions change.