AWS Certified Data Engineer Associate · Free Practice Question Medium
Question 50
A retail company is using Amazon Redshift as their data warehouse for analytical queries. They have large volumes of transactional data being generated by their online store and stored in Amazon S3. They want to efficiently load this data into Amazon Redshift for daily batch processing. The company requires a solution that automates data transformation, improves query performance, and reduces operational overhead. Which of the following solutions will best meet the company's requirements?
-
A
Use Amazon EMR to process and transform the data in S3, then copy the results into Amazon Redshift using the COPY command.
-
B
Use Amazon Redshift Spectrum to query the data directly in S3 and create materialized views for frequently queried data.
-
C
Use AWS Glue to transform data in S3 and load it directly into Redshift using AWS Glue ETL jobs.
-
D
Use the Redshift Data API to stream data from S3 into Redshift and trigger transformations using Lambda functions.
Reveal correct answer
Correct answer: C
A.
While Amazon EMR is capable of transforming data, it introduces higher operational overhead compared to AWS Glue's serverless architecture, which is more suitable for automation.
B.
Amazon Redshift Spectrum allows querying S3 data directly without loading it into Redshift, which avoids the loading step. However, creating materialized views is not sufficient to completely meet the transformation and performance optimization needs of the company.
C.
AWS Glue can perform extract, transform, and load (ETL) operations from S3 into Amazon Redshift, making it ideal for automating the transformation of large data sets and improving performance through pre-transforming data into a query-optimized format like Parquet.
D.
The Redshift Data API is not suitable for large batch loads. Additionally, using Lambda for transformations would not scale well with large volumes of data, making it an inefficient solution.
Discussion
Think the marked answer is wrong, or have a better explanation? Share it below — comments appear after review.
