AWS Certified Data Engineer – Associate (DEA-C01) — Question 74

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?

Answer options

• 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.

Correct answer: C

Explanation

The correct answer, C, minimizes operational overhead by allowing the data to be directly mapped and accessed in Amazon Redshift through an external schema and automatically refreshed materialized views. Option A requires additional steps to stage data in S3, increasing complexity. Option B involves manual refreshing of materialized views, which adds to the operational load. Option D, while effective, also adds an unnecessary layer by involving Kinesis Data Firehose for staging.