Great Expectations: Unit Tests for Your Data
Great Expectations brings unit testing to your data, letting you assert what a dataset should look like. It validates data within a pipeline, preventing bad data from corrupting models or reports.
Great Expectations brings unit testing to data engineering. You write declarative "Expectations" to test the data itself, like `expect_column_values_to_be_between(col, 1, 6)`. It's used as a step within a pipeline (e.g., in Airflow) to validate incoming data or confirm transformations are correct, automatically generating data quality reports. The key footgun is mistaking it for a pipeline orchestrator or data versioning tool; it only validates data and integrates with tools that handle those jobs.
Read the original → docs.greatexpectations.io
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