AWS Certified Data Analytics – Specialty — Question 43

A financial company uses Amazon S3 as its data lake and has set up a data warehouse using a multi-node Amazon Redshift cluster. The data files in the data lake are organized in folders based on the data source of each data file. All the data files are loaded to one table in the Amazon Redshift cluster using a separate
COPY command for each data file location. With this approach, loading all the data files into Amazon Redshift takes a long time to complete. Users want a faster solution with little or no increase in cost while maintaining the segregation of the data files in the S3 data lake.
Which solution meets these requirements?

Answer options

• A. Use Amazon EMR to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.
• B. Load all the data files in parallel to Amazon Aurora, and run an AWS Glue job to load the data into Amazon Redshift.
• C. Use an AWS Glue job to copy all the data files into one folder and issue a COPY command to load the data into Amazon Redshift.
• D. Create a manifest file that contains the data file locations and issue a COPY command to load the data into Amazon Redshift.

Correct answer: D

Explanation

The correct answer is D because creating a manifest file allows Amazon Redshift to efficiently load multiple data files in a single COPY command, significantly speeding up the process while maintaining file organization. The other options either do not effectively leverage Redshift's capabilities for bulk loading or introduce additional complexity and potential costs, which do not align with the user's requirements for a faster solution without increased expenses.