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Exam AWS Certified Data Engineer - Associate DEA-C01 topic 1 question 27 discussion

Exam question from Amazon's AWS Certified Data Engineer - Associate DEA-C01
Question #: 27
Topic #: 1
[All AWS Certified Data Engineer - Associate DEA-C01 Questions]

A company wants to implement real-time analytics capabilities. The company wants to use Amazon Kinesis Data Streams and Amazon Redshift to ingest and process streaming data at the rate of several gigabytes per second. The company wants to derive near real-time insights by using existing business intelligence (BI) and analytics tools.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use Kinesis Data Streams to stage data in Amazon S3. Use the COPY command to load data from Amazon S3 directly into Amazon Redshift to make the data immediately available for real-time analysis.
  • B. Access the data from Kinesis Data Streams by using SQL queries. Create materialized views directly on top of the stream. Refresh the materialized views regularly to query the most recent stream data.
  • C. Create an external schema in Amazon Redshift to map the data from Kinesis Data Streams to an Amazon Redshift object. Create a materialized view to read data from the stream. Set the materialized view to auto refresh.
  • D. Connect Kinesis Data Streams to Amazon Kinesis Data Firehose. Use Kinesis Data Firehose to stage the data in Amazon S3. Use the COPY command to load the data from Amazon S3 to a table in Amazon Redshift.
Show Suggested Answer Hide Answer
Suggested Answer: C 🗳️
by Aesthet at Feb. 6, 2024, 12:18 p.m.
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blackgamer
Highly Voted 1 year ago
Selected Answer: C
The answer is C. It can provide near real-time insight analysis. Refer the article from AWS - https://aws.amazon.com/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/
upvoted 8 times
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helpaws
Highly Voted 1 year, 1 month ago
Selected Answer: C
Key word here is near real-time. If it's involve S3 and COPY, it's not gonna be near real-time
upvoted 7 times
markill123
7 months, 1 week ago
Redshift cannot create external schemas that map directly to Kinesis Data Streams. You would still need an intermediary step, such as Firehose or S3, to handle data ingestion. Additionally, maintaining auto-refreshing materialized views directly from a stream isn't feasible with Redshift.
upvoted 6 times
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melligeri
Most Recent 3 weeks, 2 days ago
Selected Answer: C
https://aws.amazon.com/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/#:~:text=Before%20the%20launch,the%20data%20stream.
upvoted 1 times
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Rpathak4
3 weeks, 6 days ago
Selected Answer: D
✅ Use Kinesis Data Firehose to load data into Redshift via S3 for the simplest and most scalable solution. ✅ Firehose automatically batches, transforms, and loads data with no manual intervention required. ✅ Achieves near real-time analytics with minimal operational effort.
upvoted 2 times
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MephiboshethGumani
1 month ago
Selected Answer: D
Creating an external schema and using materialized views directly on top of Kinesis Data Streams is also not an ideal choice because this approach can add complexity and doesn't leverage fully managed solutions like Kinesis Data Firehose. The manual management of data refresh rates adds operational overhead.
upvoted 1 times
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Eltanany
1 month, 1 week ago
Selected Answer: C
Refer to the article from AWS - https://aws.amazon.com/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/
upvoted 1 times
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jesusmoh
1 month, 2 weeks ago
Selected Answer: D
option D provides a streamlined, efficient, and low-overhead approach to achieving real-time analytics with the specified technologies.
upvoted 1 times
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plutonash
3 months, 1 week ago
Selected Answer: D
A: Kinesis Data Streams to stage data in Amazon S3. not really easy, B: sql directly to Kinesis Data Streams : functionality not exist C : external schema from redshift to Kinesis Data Streams : functionality not exist D : near real-time = Kinesis Data Firehose
upvoted 2 times
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subbie
3 months, 1 week ago
Selected Answer: C
https://aws.amazon.com/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/
upvoted 1 times
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subbie
3 months, 1 week ago
Selected Answer: B
https://aws.amazon.com/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/
upvoted 1 times
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haby
3 months, 4 weeks ago
Selected Answer: A
A for me C - Redshift does not natively support direct mapping to Kinesis Data Streams. Some extra configs are needed. D - There will be a 60s latency when using Firehose, so it's "Near" real time not real time.
upvoted 1 times
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HagarTheHorrible
4 months ago
Selected Answer: D
Redshift does not natively support direct mapping to Kinesis Data Streams. Materialized views cannot directly query streaming data from Kinesis.
upvoted 1 times
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altonh
4 months, 2 weeks ago
Selected Answer: C
See https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-streaming-ingestion-getting-started.html
upvoted 1 times
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Asen_Cat
5 months, 2 weeks ago
Selected Answer: D
D could be the most standard way to handle this case. How to use C to implement it is questionable for me.
upvoted 2 times
Asmunk
5 months ago
https://docs.aws.amazon.com/streams/latest/dev/using-other-services-redshift.html
upvoted 1 times
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heavenlypearl
5 months, 2 weeks ago
Selected Answer: C
Amazon Redshift can automatically refresh materialized views with up-to-date data from its base tables when materialized views are created with or altered to have the autorefresh option. Amazon Redshift autorefreshes materialized views as soon as possible after base tables changes. https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-refresh.html
upvoted 1 times
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royalrum
5 months, 2 weeks ago
Firehose is Near-Real time, you can set your buffer size and stream to either Redshift or S3 directly. Since Redshift is not in the option, use s3...
upvoted 1 times
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Shatheesh
5 months, 3 weeks ago
Selected Answer: D
Kinesis Data Streams , option D using Kinesis Data Firehose is a fully managed service that automatically handles the ingestion of data
upvoted 1 times
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