Exam AWS Certified Machine Learning - Specialty topic 1 question 194 discussion

I think the answer is B. The others are quite expensive and complicated.

A. NO - B is easier B. YES - works natively against S3 C. NO - no need to import S3 data to Redshift when Presto/Athena allows you to query directly D. NO - Redshift overkill

AWS Glue

We want to use SQL to explore the 500GB database saved in S3. We also want to minimize costs and have the least headache with the operation. Letters A - C - D mean managed services, hence: headache. Correct alternative is letter B.

B as "LEAST operational overhead"

The advantage of D is that Redshift, as a data warehouse, can handle large dataset(>1TB) and complex frequent query. In this example, 500GB dataset and infrequent query(I consider this just one-time ad-hoc query, just verify the data before training.) Athena would be a much better option.

The option that meets these requirements with the LEAST operational overhead is option B: Use AWS Glue to crawl the S3 bucket and create tables in the AWS Glue Data Catalog. Use Amazon Athena to explore the data. AWS Glue is a fully managed ETL service that can automatically discover and catalog metadata about data stored in various data stores, including Amazon S3. By using AWS Glue to crawl the S3 bucket, the data scientist can easily create tables in the AWS Glue Data Catalog, without needing to create or manage any infrastructure. Amazon Athena is an interactive query service that allows querying data stored in Amazon S3 using SQL. By using Amazon Athena, the data scientist can easily explore the data using SQL, without needing to set up any infrastructure.

Both Glue and Athena are serverless hence cost effective.

B is highly managed unlike other options.

It seems to be B