# Monitor data quality in data processing jobs
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 When a Visual ETL job includes an Evaluate Data Quality transform, the data quality results are captured and displayed on the data processing jobs page. This gives you a centralized view of quality checks that ran as part of your data integration pipeline. 

 Rules are written using DQDL (Data Quality Definition Language), a domain-specific language for defining data quality rules, with 31 built-in rule types. For details on configuring the Evaluate Data Quality transform, defining rules, and selecting outputs, see [Evaluate data quality transform](evaluate-data-quality-transform.md). For the full list of rule types and syntax, see [DQDL rule types](https://docs.aws.amazon.com/glue/latest/dg/dqdl-rule-types.html) in the AWS Glue documentation.