Which combination of solutions will meet these requirements with the LEAST operational overhead?
(Choose three.)
Use Amazon Athena to scan the data and identify the schema.
Use AWS Glue crawlers to scan the data and identify the schema.
Use Amazon Redshift to store procedures to perform data transformations.
Use AWS Glue workflows and AWS Glue jobs to perform data transformations.
Use Amazon Redshift ML to train a model to detect fraud.
Use Amazon Fraud Detector to train a model to detect fraud.
Explanations:
While Amazon Athena can scan data and provide results based on SQL queries, it does not automatically identify schemas from unstructured data without prior configuration. It is less efficient for schema discovery compared to AWS Glue crawlers.
AWS Glue crawlers can automatically scan the data stored in Amazon S3, infer the schema, and create tables in the AWS Glue Data Catalog, making it an effective solution for schema identification with minimal operational overhead.
Amazon Redshift is primarily a data warehousing solution and is not designed for schema identification or lightweight data transformation processes. Using stored procedures also involves higher operational overhead.
AWS Glue workflows and jobs can automate the process of transforming data in Amazon S3, providing a serverless and managed environment, which significantly reduces operational overhead.
While Amazon Redshift ML allows you to build and train models using SQL queries within Redshift, it is not the best choice for fraud detection when compared to more specialized solutions like Amazon Fraud Detector. It also adds more complexity.
Amazon Fraud Detector is a fully managed service that simplifies the process of building, training, and deploying machine learning models for fraud detection, offering an efficient and effective solution with low operational overhead.
If memory serves me right, the answer is:
Use AWS Glue crawlers to scan the data and identify the schema.