Great Expectations Data Quality
Add automated data-quality gates with Great Expectations. Profiles datasets, generates expectation suites, runs validations and checkpoints, and wires quality gates into pipelines to catch bad data early.
This skill implements the Great Expectations data-quality framework end to end. It profiles datasets, generates and tunes expectation suites, runs validations and checkpoints, builds data docs, and integrates quality gates into ETL and orchestration so bad data fails fast instead of corrupting downstream tables.
great-expectations data-quality validation data-testing pipelines
When to use
Use when adding data-quality validation, generating Great Expectations suites, running checkpoints, or wiring quality gates into a data pipeline.
Examples
Generate a suite
Profile and create expectations
Profile my customers table with Great Expectations and generate an expectation suite covering not-null, uniqueness, and value ranges
Pipeline quality gate
Fail on bad data
Add a Great Expectations checkpoint to my Airflow DAG that halts the pipeline if the daily load violates the expectation suite
Validate a load
Run a checkpoint
Run my Great Expectations checkpoint against today's data and summarize which expectations failed