Least Overhead ETL to Redshift
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?
Community Votes
67% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The key is identifying 'serverless' tools that handle both transformation (DataBrew) and orchestration/loading (Glue) without manual infrastructure management.
AWS Glue DataBrew and AWS Glue provide a serverless solution for data preparation and loading into Amazon Redshift, eliminating the need to manage infrastructure or perform complex custom ETL coding.
Candidates often choose D because they overlook that AWS DMS is primarily for database-to-database migration and does not natively support S3 as a direct source for schema-less raw data without additional glue code.
Community Discussion (4 comments)
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Expert Analysis
Why the Answer Is Correct
Option B is correct because it leverages fully managed, serverless services. AWS Glue DataBrew is a visual data preparation tool that requires no code and handles cleaning/transformation efficiently. AWS Glue provides serverless ETL jobs to load this prepared data into Amazon Redshift. This combination eliminates infrastructure management (EC2/EMR) and reduces operational overhead compared to other options.Why the Other Options Are Wrong
Option A uses Amazon EMR, which requires managing clusters (even if using EMR Serverless, it's more complex than Glue for simple tasks) and Step Functions adds orchestration overhead. Option C suggests using Lambda for terabytes of data, which hits memory/time limits, and Athena is for querying S3 directly, not running queries on loaded Redshift data as implied by the workflow. Option D claims DMS supports S3; while recent updates allow some S3 integration, it is not the standard pattern for general-purpose data cleaning/prep like Glue DataBrew, and DMS is typically for homogeneous/heterogeneous DB replication.Community Comment Notes
One user noted that DMS does not support S3 as a source in the context of this exam question, validating why D is incorrect. Another user emphasized that Redshift allows for 'complex' queries, supporting the use of Redshift as the analytics engine in option B. The community consensus leans heavily towards B due to the 'least operational overhead' requirement favoring fully managed services.Exam Strategy
When asked for 'least operational overhead', prioritize Fully Managed/Serverless services (Glue, Lambda, Fargate) over self-managed or cluster-based ones (EMR, EC2). For data prep, look for specific tools like DataBrew or Glue Studio before writing custom code.
Frequently Asked Questions
Why isn't AWS DMS used for loading from S3?
AWS DMS is designed for migrating data between databases. While it has some S3 capabilities, Glue is the native, serverless choice for general ETL from S3 to Redshift.
Can I use Lambda for terabytes of data?
No, Lambda has strict time and memory limits (max 15 mins, 10GB RAM). It is unsuitable for processing terabytes of raw data.
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