A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data

Writing a custom Python ETL job involves significant operational overhead for development, deployment, and maintenance of the custom code and infrastructure, and `hashlib` is typically used for cryptographic hashing, not for semantic deduplication of records.

AWS Glue's FindMatches machine learning transform is specifically designed to identify duplicate records, even if they are not exact matches (fuzzy matching), providing a managed and serverless solution that minimizes operational overhead for deduplication tasks.

Writing a custom Python ETL job and importing the `dedupe` library requires managing custom infrastructure and library dependencies, leading to higher operational overhead compared to a fully managed service feature.

While using AWS Glue for the ETL job is good, importing and managing an external Python library like `dedupe` within Glue is generally more complex and has higher operational overhead than using native Glue ML transforms like FindMatches for deduplication.