A data engineer must use AWS services to ingest a dataset into an Amazon S3 data lake. The data engineer profiles the dataset and discovers that the dataset contains personally identifiable informatio

Using Amazon Kinesis Data Firehose with AWS Lambda requires custom code for PII detection and obfuscation, leading to higher operational effort compared to AWS Glue's built-in transforms.

While it mentions the "Detect PII transform," the obfuscation step is vague and doesn't explicitly leverage AWS Glue's integrated features for applying transformations based on detected PII, potentially implying more manual effort.

This solution uses AWS Glue Studio's native "Detect PII" transform for identifying sensitive data, and AWS Glue jobs can then apply obfuscation based on these detections or configured rules, providing a managed, low-code approach with minimal operational overhead. AWS Glue is specifically designed for ETL tasks, including data profiling and transformations like PII detection and redaction, making it the most integrated and least operational effort option.

Ingesting data into Amazon DynamoDB is not a typical step for an Amazon S3 data lake, and using AWS Lambda for PII detection and obfuscation involves custom development and higher operational effort.