An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog. Data analysts want to join this data with call center data stored in Amazon Redshift as part of a dally batch process. The Amazon Redshift cluster is already under a heavy load. The solution must be managed, serverless, well-functioning, and minimize the load on the existing Amazon Redshift cluster. The solution should also require minimal effort and development activity. Which solution meets these requirements?
A) Unload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function. Perform the join with AWS Glue ETL scripts.
B) Export the call center data from Amazon Redshift using a Python shell in AWS Glue. Perform the join with AWS Glue ETL scripts.
C) Create an external table using Amazon Redshift Spectrum for the call center data and perform the join with Amazon Redshift.
D) Export the call center data from Amazon Redshift to Amazon EMR using Apache Sqoop. Perform the join with Apache Hive.

Question

Quizplus · Accepted Answer

Amazon Redshift Spectrum allows you to query data stored in Amazon S3 using SQL queries. This eliminates the need to load the data into Amazon Redshift, reducing the load on the cluster.

An Airline Has