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Answer: Use AWS Glue DataBrew to prepare the data. Use AWS Glue to load the data into Amazon Redshift. Use Amazon Redshift to run queries.
Option B is CORRECT because using AWS Glue DataBrew allows the data engineer to visually clean and prepare the data without writing complex ETL code. It provides an easy-to-use interface for data transformation, which reduces the complexity of the ETL process. AWS Glue can then be used to load the prepared data into Amazon Redshift. This approach eliminates the need for managing infrastructure, as both Glue and DataBrew are serverless services. Once the data is loaded, analysts can run complex queries directly in Amazon Redshift, which is designed for high-performance data analysis and querying.
Author: Ritesh Yadav
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Question 8/58
A data engineer is processing and analyzing multiple terabytes of raw data that is in Amazon S3. The data engineer needs to clean and prepare the data. Then the data engineer needs to load the data into Amazon Redshift for analytics.
The data engineer needs a solution that will give data analysts the ability to perform complex queries. The solution must eliminate the need to perform complex extract, transform, and load (ETL) processes or to manage infrastructure.
Which solution will meet these requirements with the LEAST operational overhead?
A
Use Amazon EMR to prepare the data. Use AWS Step Functions to load the data into Amazon Redshift. Use Amazon QuickSight to run queries.
B
Use AWS Glue DataBrew to prepare the data. Use AWS Glue to load the data into Amazon Redshift. Use Amazon Redshift to run queries.
C
Use AWS Lambda to prepare the data. Use Amazon Kinesis Data Firehose to load the data into Amazon Redshift. Use Amazon Athena to run queries.
D
Use AWS Glue to prepare the data. Use AWS Database Migration Service (AVVS DMS) to load the data into Amazon Redshift. Use Amazon Redshift Spectrum to run queries.
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