Which solution will meet these requirements?
Use an AWS Lambda function to process the data. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates.
Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Call the AWS Glue SearchTables API operation to perform a fuzzy- matching search on the two datasets, and cleanse the data accordingly.
Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.
Create an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.
Explanations:
Using AWS Lambda with arrays to compare strings and remove duplicates is not an efficient or scalable solution for combining datasets. It would likely require complex custom code, and managing large datasets would be difficult.
While AWS Glue crawlers and the SearchTables API could help in identifying datasets, the fuzzy matching and cleansing process cannot be directly done with this API. Additional custom logic would be needed.
AWS Glue crawlers can populate the Data Catalog, and the FindMatches transform is specifically designed to identify and combine similar records across datasets. It performs fuzzy matching and removes duplicates automatically.
AWS Lake Formation is not designed for this task. It provides data governance features but does not have a built-in transform specifically for matching and cleansing product data.
I have a feeling that the answer is:
Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.
I classify that the answer is:
Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.