An investment firm ingests an ever-growing volume of semi-structured data and needs to deduplicate records, removing duplicates and common misspellings. Which option provides this capability with the least operational overhead?

The FindMatches feature in AWS Glue is specifically designed for identifying and deduplicating records in datasets, even when exact matches are not present (e.g., due to misspellings or variations). It uses machine learning to learn patterns and identify records that refer to the same real-world entity, making it ideal for semi-structured data and fuzzy matching. This approach minimizes operational overhead as Glue manages the underlying infrastructure. Using non-window functions in Amazon Athena could remove exact duplicates but wouldn't handle misspellings or variations effectively. Amazon Neptune ML and Apache Gremlin are for graph databases and graph analytics, not primarily for data deduplication of semi-structured data. Global tables in Amazon DynamoDB provide multi-region replication and high availability for key-value data but do not prevent or resolve duplicate data within the tables themselves.