You work on a machine learning team at a manufacturing company that produces fire detection products. You are building a fire detection analytics model, the source data store of which has structured and unstructured data stored in an S3 bucket. You are in the data engineering and data analysis phase of the machine learning lifecycle. At this point, you need to use SQL to run queries on your source data to determine feature correlation and dimensionality. Which option allows you to query the data with the least amount of effort?

Answer options:

A.Run queries using Kinesis Data Analytics after using a Lambda function to transform the data.
B.Use the AWS Batch service to extract, transform, and load (ETL) the data and an ElasticSearch cluster to run the queries.
C.Use a Glue crawler to catalog the data and Athena to query the data on S3.
D.Query the data in RDS after using Data Pipeline to transform the data and load it into RDS. 

Correct Answer: C
Option A is incorrect. Transforming the data using a Lambda function and then querying the data in Kinesis Data Analytics requires you to write a Lambda function. This is more effort than crawling the data using a Glue crawler then using Athena to query the data directly on S3.
Option B is incorrect. Using AWS Batch to perform ETL on the data and then loading the data into an ElasticSearch cluster requires more effort than crawling the data using a Glue crawler, then using Athena to query the data directly on S3.
Option C is correct. Crawling the data using a Glue crawler and then querying the data directly on S3 using Athena only requires you to write your SQL code. You don’t need to write a Lambda function or create an Aurora or RDS database.
Option D is incorrect. With this option, you have to create an RDS database and then load your data into the RDS instance using Data Pipeline. This requires more effort than crawling the data using a Glue crawler then using Athena to query the data directly on S3.
References:
Please see the AWS Data Pipeline developer guide titled What is AWS Data Pipeline? (https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/what-is-datapipeline.html),
The AWS Glue developer guide titled Defining Crawlers (https://docs.aws.amazon.com/glue/latest/dg/add-crawler.html),
The AWS Batch user guide titled What Is AWS Batch? (https://docs.aws.amazon.com/batch/latest/userguide/what-is-batch.html),
The Amazon Kinesis Data Analytics for SQL Applications Developer Guide SQL developer guide titled What Is Amazon Kinesis Data Analytics for SQL Applications? (https://docs.aws.amazon.com/kinesisanalytics/latest/dev/what-is.html)