Data quality checks are now built into Visual ETL in Amazon SageMaker Unified Studio, so data engineers can validate data as part of their pipelines. With the Evaluate Data Quality transform, you can define rules, check incoming data against these rules, and send the results down your flow. You can catch bad records early, block problems from reaching downstream consumers, and keep your analytics and machine learning work on trusted data. All of this happens on the same visual canvas you already use to build your ETL jobs.
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