Case Study: Calm averts critical data issues with Great Expectations

How Calm uses GX to create data quality alerts and avert critical data issues in Airflow DAGs

Calm, the #1 app for sleep, meditation, and relaxation, needed a better way to catch data quality issues before stakeholders did. Before adopting Great Expectations, validation was ad hoc and mostly limited to simple row-count checks, which led to late detection of problems and frequent data fires.

With Great Expectations, Calm built semi-automated Expectation Suite creation from table DDLs and added a custom Airflow operator to validate staging tables against those suites. The workflow let pipelines continue, warn in Slack, or fail critical tasks based on severity, helping Calm stay ahead of data issues and giving stakeholders more confidence that decisions were based on accurate data.

Calm

Kamla Kasichainula

Data Engineering Team