A manufacturing company uses AWS Glue jobs to process IoT sensor data to generate predictive...

Create an AWS Glue DataBrew project to profile the sensor data. Define completeness rules for timestamps. Set up numeric range validation for temperature values.

Use AWS Glue's Data Quality rules and machine learning (ML)-based anomaly detection to identify missing timestamps and to detect temperature anomalies.

Create an AWS Lambda function to scan the sensor data files to validate temperature ranges. Use AWS Glue Data Catalog tables to check timestamp completeness.

Create an AWS Glue DynamicFrame that uses a custom data quality operator to profile the sensor data. Use Amazon SageMaker Data Wrangler transforms to validate timestamps and temperature ranges.