Q29Data Ingestion and Transformation
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?
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Community votes
Discussion · 19
C 11
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/
C 8
Selected Answer: C
The key word here is near real-time. If it's involves S3 and COPY, it's not gonna be near real-time
7
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. Also, maintaining auto-refreshing materialized views directly from a stream isn't feasible with Redshift.
D 3
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.
D 3
Selected Answer: D
✅ Use Kinesis Data Firehose to load data into Redshift through S3 for the simplest and most scalable approach.
✅ Firehose automatically batches, transforms, and loads data with no manual intervention needed.
✅ Delivers near real-time analytics with minimal operational effort.
D 2
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
D 1
Selected Answer: D
Kinesis Data Streams , option D using Kinesis Data Firehose is a fully managed service that automatically handles the ingestion of data
1
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...
C 1
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
1
https://docs.aws.amazon.com/streams/latest/dev/using-other-services-redshift.html
C 1
Selected Answer: C
See https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-streaming-ingestion-getting-started.html
D 1
Selected Answer: D
Redshift does not natively support direct mapping to Kinesis Data Streams. Materialized views cannot directly query streaming data from Kinesis.
A 1
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.
B 1
Selected Answer: B
https://aws.amazon.com/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/
C 1
Selected Answer: C
https://aws.amazon.com/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/
D 1
Selected Answer: D
option D gives a streamlined, efficient, and low-overhead way to achieve real-time analytics with the specified technologies.
C 1
Selected Answer: C
Refer to the article from AWS - https://aws.amazon.com/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/
D 1
Selected Answer: D
Creating an external schema and using materialized views directly on top of Kinesis Data Streams is also not the best choice because this approach adds complexity and doesn't fully use managed services like Kinesis Data Firehose. The manual handling of data refresh rates increases operational overhead.
C 1
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.