How to stream Kinesis Data Streams to Redshift Serverless with least overhead?
A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records. A data engineer needs to find a solution to process the streaming data. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data. Which solution will meet these requirements with the LEAST operational overhead?
Community Votes
100% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
Tests whether you know Redshift streaming ingestion reads Kinesis Data Streams directly; the trap is selecting Kinesis Data Firehose or S3 COPY, which add latency and intermediate steps.
This DEA-C01 question asks how to feed Kinesis Data Streams into Amazon Redshift Serverless for near real-time analytics of current and previous-day data with the least operational overhead. Redshift streaming ingestion is correct (B) because it consumes Kinesis directly into materialized views without extra ETL services.
Many candidates choose Kinesis Data Firehose (A) as the familiar Redshift loading path, but Firehose buffers through S3 and COPY and is not the least-overhead near-real-time solution.
Community Discussion (4 comments)
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Expert Analysis
Why the Answer Is Correct
Option B is correct because Amazon Redshift streaming ingestion consumes records directly from Kinesis Data Streams into materialized views, enabling near real-time queries without managing intermediate services. The same Redshift Serverless warehouse can store historical tables and join them with streaming materialized views, satisfying the requirement for both current and previous-day analytics. Because this is a native Redshift capability, it minimizes the operational overhead compared with Firehose-plus-COPY pipelines. Community commenter rralucard_ linked the official AWS documentation and highlighted that the feature "simplifies the architecture by eliminating the need for intermediate steps or services," and other learners (ssnei, 4c78df0, lucas_rfsb) also selected B.Why the Other Options Are Wrong
Option A places Kinesis Data Firehose in the path, but Firehose buffers records and typically delivers to Redshift through S3 and COPY, which adds latency and extra components. Option C uses Amazon S3 plus the COPY command, a batch-oriented approach that cannot provide near real-time analytics and requires you to manage staging and load scheduling. Option D uses the Aurora zero-ETL integration with Amazon Redshift, which is designed for replicating Aurora transactional data rather than ingesting Kinesis Data Streams. Therefore, none of these alternatives meet the near real-time and least-operational-overhead requirements as directly as option B.Community Comment Notes
The most upvoted comment, from rralucard_ with 7 likes, provides the AWS documentation link for materialized-view streaming ingestion and notes that it "simplifies the architecture by eliminating the need for intermediate steps or services." Several other commenters (ssnei, 4c78df0, and lucas_rfsb) also answered option B, reinforcing that Redshift streaming ingestion is the expected DEA-C01 pattern for this scenario. No comment argued for Firehose, S3 COPY, or Aurora zero-ETL as the least-overhead solution. The linked AWS documentation remains the authoritative reference for the feature tested here.Official Reference
Exam Strategy
For "least operational overhead" questions, eliminate options that require you to manage intermediate storage, ETL jobs, or batch schedules. Redshift streaming ingestion is a native feature that reads Kinesis Data Streams directly, so it beats Firehose plus COPY and Aurora zero-ETL, which targets transactional databases instead of streaming ingestion.
Frequently Asked Questions
Why is Kinesis Data Firehose not the least-overhead choice here?
Firehose delivers to Redshift only through S3 and COPY, adding buffering, extra storage, and pipeline management. Redshift streaming ingestion removes those intermediate steps.
Can Redshift query both streaming data and the previous day's data?
Yes. Streaming ingestion populates materialized views for near real-time data, while the same Redshift Serverless warehouse stores and queries historical tables for yesterday's data.
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