Enabling Data Quality with Dagster and Great Expectations

Dagster and Great Expectations are used to improve the reliability of data pipelines by integrating data quality checks. This allows for the detection of erroneous or malformed data before it is propagated downstream, ensuring that stakeholder-facing products such as dashboards and reports receive accurate and reliable data. By combining Dagster's Asset Checks with Great Expectations' extensive suite of data quality tests, developers can simplify their testing logic without sacrificing the reliability of their data pipelines. The integration enables users to define expectations for their data assets using a more straightforward approach, reducing the amount of custom code required for testing and maintaining data quality.

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