Which option will meet the company's requirements?
A. Develop a script that uses Amazon Athena to query and analyze the files on Amazon S3. Then use Amazon QuickSight to connect to Athena and perform the visualization.
B. Develop a stored procedure invoked from a MySQL database running on Amazon EC2 to analyze EC2 to analyze the files in Amazon S3. Then use a fast in-memory BL tool running on Amazon EC2 to visualize the data.
C. Use a commercial extract, transform, load (ETL) tool that runs on Amazon EC2 to prepare the data for processing. Then switch to a faster and cheaper Bl tool that runs on Amazon EC2 to visualize the data from Amazon S3.
D. Launch a transient Amazon EMR cluster daily and develop an Apache Hive script to analyze the files on Amazon S3. Shut down the Amazon EMR cluster when the job is complete. The use the Amazon QuickSight to connect to Amazon EMR and perform the visualization.
Answer:A
Which option will meet the company's requirements?
A. Develop a script that uses Amazon Athena to query and analyze the files on Amazon S3. Then use Amazon QuickSight to connect to Athena and perform the visualization. B. Develop a stored procedure invoked from a MySQL database running on Amazon EC2 to analyze EC2 to analyze the files in Amazon S3. Then use a fast in-memory BL tool running on Amazon EC2 to visualize the data. C. Use a commercial extract, transform, load (ETL) tool that runs on Amazon EC2 to prepare the data for processing. Then switch to a faster and cheaper Bl tool that runs on Amazon EC2 to visualize the data from Amazon S3. D. Launch a transient Amazon EMR cluster daily and develop an Apache Hive script to analyze the files on Amazon S3. Shut down the Amazon EMR cluster when the job is complete. The use the Amazon QuickSight to connect to Amazon EMR and perform the visualization.
Explanation: https://docs.aws.amazon.com/quicksight/latest/user/create-a-data-set-athena.html https://aws.amazon.com/athena/